Сөздік ендірулер

. Information Extraction systems must cope with two problems : they heavily depend on the considered domain but the cost of development for a domain-specific system is important. We propose …...

AbstractCurrent supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can …...

Recent work has shown success in learning word embeddings with neural network language models (NNLM). However, the majority of previous NNLMs represent each word with a single embedding, which fails …...

We propose a neural network approach to benefit from the non-linearity of corpus- wide statistics for part-of-speech (POS) tagging. We investigated several types of corpus-wide information for the words, such …...

The state-of-the-art methods used for relation classification are primarily based on statistical ma- chine learning, and their performance strongly depends on the quality of the extracted features. The extracted features …...

We present a novel technique for semantic frame identification using distributed rep- resentations of predicates and their syntac- tic context; this technique leverages auto- matic syntactic parses and a generic …...

The paper describes our experiments ad- dressing the SemEval 2014 task on the Analysis of Clinical text. Our approach consists in extending the techniques of NE recognition, based on sequence …...

We propose to use coreference chains ex- tracted from a large corpus as a resource for semantic tasks. We extract three mil- lion coreference chains and train word embeddings on …...

Word embeddings resulting from neural language models have been shown to be a great asset for a large variety of NLP tasks. However, such architecture might be difficult and time-consuming …...

This paper presents the first attempt to use word embeddings to predict the composition- ality of multiword expressions. We consider both single- and multi-prototype word em- beddings. Experimental results show …...

This study investigates the use of unsuper- vised features derived from word embed- ding approaches and novel sequence rep- resentation approaches for improving clin- ical information extraction systems. Our results …...

ROUGE is a widely adopted, automatic evaluation measure for text summariza- tion. While it has been shown to corre- late well with human judgements, it is bi- ased towards surface …...

Word embeddings — distributed word representations that can be learned from unlabelled data — have been shown to have high utility in many natural language processing applications. In this paper, …...

Compositional embedding models build a rep- resentation for a linguistic structure based on its component word embeddings. While re- cent work has combined these word embed- dings with hand crafted …...

This paper presents a detailed compar- ative framework for assessing the use- fulness of unsupervised word representa- tions for identifying so-called implicit dis- course relations. Specifically, we compare standard one-hot …...

This paper introduces a convolutional sen- tence kernel based on word embeddings. Our kernel overcomes the sparsity issue that arises when classifying short docu- ments or in case of little …...

Word embeddings have been found useful for many NLP tasks, including part-of-speech tagging, named entity recognition, and pars- ing. Adding multilingual context when learn- ing embeddings can improve their quality, …...

The tasks in fine-grained opinion mining can be regarded as either a token-level se- quence labeling problem or as a semantic compositional task. We propose a gen- eral class of …...

Linguistic similarity is multi-faceted. For in- stance, two words may be similar with re- spect to semantics, syntax, or morphology in- ter alia. Continuous word-embeddings have been shown to capture …...

Word embeddings encode semantic mean- ings of words into low-dimension word vectors. In most word embeddings, one cannot interpret the meanings of specific dimensions of those word vectors. Non- negative …...

The high-dimensionality of lexical features in parsing can be memory consuming and cause over-fitting problems. We propose a general framework to replace all lexical feature tem- plates by low-dimensional features …...

Sarcasm is generally characterized as a figure of speech that involves the substi- tution of a literal by a figurative mean- ing, which is usually the opposite of the original …...

Semi-supervised bootstrapping techniques for relationship extraction from text iter- atively expand a set of initial seed rela- tionships while limiting the semantic drift. We research bootstrapping for relationship extraction using …...

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a pop- ular area of NLP research. …...

We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific fea- tures on …...

An acid test for any new Word Sense Disam- biguation (WSD) algorithm is its performance against the Most Frequent Sense (MFS). The field of WSD has found the MFS baseline …...

Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsu- pervised problems has been less thoroughly ex- plored. In this …...

Vectorial representations of words have grown remarkably popular in natural language process- ing and machine translation. The recent surge in deep learning-inspired methods for producing distributed representations has been widely …...

This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, how- ever, cannot capture antonyms …...

AbstractA key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of …...

We investigate the usage of word embeddings, namely Glove and SCODE, along with sub- string features on Complex Word Identifica- tion task. We introduce two systems: the first system utilizes …...

This paper makes a simple increment to state-of- the-art in sarcasm detection research. Existing ap- proaches are unable to capture subtle forms of con- text incongruity which lies at the …...

AbstractSequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture …...

This paper describes our submissions to the Aspect Based Sentiment Analysis task of SemEval-2016. For Aspect Category Detec- tion (Subtask1/Slot1), we used multiple en- sembles, based on Support Vector Machine …...

This paper describes the system with which we participated in SemEval-2016 Task 4 (Sen- timent Analysis in Twitter) and specifically the Message Polarity Classification subtask. Our system is a weighted …...

AbstractWe assess the reliability and accuracy of (neural) word embeddings for both modern and historical English and German. Our research provides deeper insights into the empirically justified choice of optimal …...

AbstractParallel sentence representations are important for bilingual and cross-lingual tasks in natural language processing. In this paper, we explore a bilingual autoencoder approach to model parallel sentences. We extract sentence-level …...

We submitted two systems to the SemEval- 2016 Task 12: Clinical TempEval challenge, participating in Phase 1, where we identified text spans of time and event expressions in clinical notes …...

AbstractMethods for text simplification using the framework of statistical machine translation have been extensively studied in recent years. However, building the monolingual parallel corpus necessary for training the model requires …...

While unsupervised anaphoric zero pro- noun (AZP) resolvers have recently been shown to rival their supervised counter- parts in performance, it is relatively diffi- cult to scale them up to …...

AbstractIn this paper we consider the problem of out-of-vocabulary term classification in web forum text from the automotive domain. We develop a set of nine domain- and application-specific categories for …...

AbstractWhen processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural …...

AbstractIn this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number …...

AbstractAspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning …...

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture se- mantic information from massive amounts of textual content. As …...

AbstractCanonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates …...

AbstractWord embeddings have recently seen a strong increase in interest as a result of strong performance gains on a variety of tasks. However, most of this research also underlined the …...

AbstractCollocations such as “heavy rain” or “make [a] decision”, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, …...

In this paper, we utilize distributed word rep- resentations (i.e., word embeddings) to anal- yse the representation of semantics in brain activity. The brain activity data were recorded using functional …...

Word pairs, which are one of the most eas- ily accessible features between two text segments, have been proven to be very useful for detecting the discourse relations held between …...

AbstractGaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space. This can take semantic information of …...

Mapping word embeddings of different lan- guages into a single space has multiple appli- cations. In order to map from a source space into a target space, a common approach …...

Word embeddings – distributed represen- tations of words – in deep learning are beneficial for many tasks in NLP. How- ever, different embedding sets vary greatly in quality and characteristics …...

SemEval 2016 Task 12 addresses temporal reasoning in the clinical domain. In this paper, we present our participation for relation ex- traction based on gold standard entities (sub- tasks DR …...

We introduce a novel, simple convolution neu- ral network (CNN) architecture – multi-group norm constraint CNN (MGNC-CNN) – that capitalizes on multiple sets of word embed- dings for sentence classification. …...

Word embeddings allow natural language pro- cessing systems to share statistical information across related words. These embeddings are typically based on distributional statistics, mak- ing it difficult for them to …...

AbstractThis paper presents mwetoolkit+sem: an extension of the mwetoolkit that estimates semantic compositionality scores for multiword expressions (MWEs) based on word embeddings. First, we describe our implementation of vector-space operations …...

We discuss an approximate similarity search for word embeddings, which is an operation to approximately find em- beddings close to a given vector. We compared several metric-based search al- gorithms …...

AbstractTemporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose entities are …...

AbstractOut-of-vocabulary (OOV) word is a crucial problem in statistical machine translation (SMT) with low resources. OOV paraphrasing that augments the translation model for the OOV words by using the translation …...

As more historical texts are digitized, there is interest in applying natural language pro- cessing tools to these archives. However, the performance of these tools is often unsatisfac- tory, due …...

. The article introduces a transition-based constituent parsing method relying on a deep-learning weighting scheme. This weighting method is compared to a structured percep- tron, which is a more traditional …...

Continuous space word embeddings have received a great deal of atten- tion in the natural language processing and machine learning communities for their ability to model term similarity and other …...

AbstractEmotion classification from text typically requires some degree of word-emotion association, either gathered from pre-existing emotion lexicons or calculated using some measure of semantic relatedness. Most emotion lexicons contain a …...

We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high- quality sentence embeddings. Averaging the embeddings of words in a sentence …...

Lexical simplification of scientific terms rep- resents a unique challenge due to the lack of a standard parallel corpora and fast rate at which vocabulary shift along with research. We …...

AbstractTraining good word embeddings requires large amounts of data. Out-of-vocabulary words will still be encountered at test-time, leaving these words without embeddings. To overcome this lack of embeddings for rare …...

State-of-the-art word embeddings, which are often trained on bag-of-words (BOW) con- texts, provide a high quality representation of aspects of the semantics of nouns. However, their quality decreases substantially for …...

Recent work has shown that simple vector subtraction over word embeddings is surpris- ingly effective at capturing different lexical relations, despite lacking explicit supervision. Prior work has evaluated this intriguing …...

We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects perfor- mance on a wide range of intrinsic and ex- trinsic …...

We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the sub- task B. Our system comprises of multiple in- dependent models …...

AbstractMicroblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post …...

In this paper we consider several approaches to predicting semantic textual similarity us- ing word embeddings, as well as methods for forming embeddings for larger units of text. We compare …...

The paper describes our submission to the task on Sentiment Analysis on Twitter at SemEval 2016. The approach is based on a Deep Learning architecture using convolu- tional neural networks. …...

Segmenting text into semantically coher- ent fragments improves readability of text and facilitates tasks like text summariza- tion and passage retrieval. In this pa- per, we present a novel unsupervised …...

AbstractWord embeddings have been successfully used in several natural language processing tasks (NLP) and speech processing. Different approaches have been introduced to calculate word embeddings through neural networks. In the …...

AbstractSolving word analogies became one of the most popular benchmarks for word embeddings on the assumption that linear relations between word pairs (such as king:man :: woman:queen) are indicative of …...

AbstractModels work best when they are optimized taking into account the evaluation criteria that people care about. For topic models, people often care about interpretability, which can be approximated using …...

AbstractWord embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors …...

AbstractRecent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from abstractive count-based models. This paper is …...

AbstractAspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually …...

AbstractWe analyze semantic changes in loanwords from English that are used in Japanese (Japanese loanwords). Specifically, we create word embeddings of English and Japanese and map the Japanese embeddings into …...

AbstractThis paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system …...

AbstractExisting studies on semantic parsing mainly focus on the in-domain setting. We formulate cross-domain semantic parsing as a domain adaptation problem: train a semantic parser on some source domains and …...

AbstractExisting approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual …...

AbstractIn this paper we present two deep-learning systems that competed at SemEval-2017 Task 4 “Sentiment Analysis in Twitter”. We participated in all subtasks for English tweets, involving message-level and topic-based …...

AbstractIn this paper we describe experiments with automated detection of metaphors in the Polish language. We focus our analysis on noun phrases composed of an adjective and a noun, and …...

AbstractWord embeddings have become widely-used in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain …...

AbstractLearning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations …...

AbstractWe focus on non-standard usages of common words on social media. In the context of social media, words sometimes have other usages that are totally different from their original. In …...

AbstractWord embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process does not allow the different …...

AbstractWe present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the …...

AbstractTo extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to …...

AbstractWord embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for …...

AbstractIn this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and …...

AbstractThis paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has become attractive and been used in some applications recently, but …...

AbstractWe propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. …...

AbstractThis paper reports team IITPB’s participation in the SemEval 2017 Task 5 on ‘Fine-grained sentiment analysis on financial microblogs and news’. We developed 2 systems for the two tracks. One …...

AbstractWord embeddings have attracted much attention recently. Different from alphabetic writing systems, Chinese characters are often composed of subcharacter components which are also semantically informative. In this work, we propose …...

AbstractThis paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use …...

AbstractWord embeddings have been shown to be highly effective in a variety of lexical semantic tasks. They tend to capture meaningful relational similarities between individual words, at the expense of …...

AbstractNeural machine translation (NMT) is the state of the art for machine translation, and it shows the best performance when there is a considerable amount of data available. When only …...

AbstractWord embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To …...

AbstractThis paper describes our multi-view ensemble approach to SemEval-2017 Task 4 on Sentiment Analysis in Twitter, specifically, the Message Polarity Classification subtask for English (subtask A). Our system is a …...

AbstractThis paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our proposed …...

AbstractThis paper reports the details of our submissions in the task 1 of SemEval 2017. This task aims at assessing the semantic textual similarity of two sentences or texts. We …...

AbstractThis paper shows the details of our system submissions in the task 2 of SemEval 2017. We take part in the subtask 1 of this task, which is an English …...

AbstractWord embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically …...

AbstractThis paper introduces a new task on Multilingual and Cross-lingual SemanticThis paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word …...

AbstractIn this paper we introduce our system participating at the 2017 SemEval shared task on keyphrase extraction from scientific documents. We aimed at the creation of a keyphrase extraction approach …...

AbstractThe majority of core techniques to solve many problems in Community Question Answering (CQA) task rely on similarity computation. This work focuses on similarity between two sentences (or questions in …...

AbstractMany Natural Language Processing (NLP) models rely on distributed vector representations of words. Because the process of training word vectors can require large amounts of data and computation, NLP researchers …...

AbstractThe state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human …...

AbstractA common reason for errors in coreference resolution is the lack of semantic information to help determine the compatibility between mentions referring to the same entity. Distributed representations, which have …...

AbstractIn this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label classification scenario. The embeddings are used in three convolutional neural network topologies. The …...

AbstractWord embeddings seek to recover a Euclidean metric space by mapping words into vectors, starting from words co-occurrences in a corpus. Word embeddings may underestimate the similarity between nearby words, …...

AbstractIn this paper, we describe a methodology to predict emoji in tweets. Our approach is based on the classic bag-of-words model in conjunction with word embeddings. The used classification algorithm …...

AbstractThis paper describes 300-sparsians’s participation in SemEval-2018 Task 9: Hypernym Discovery, with a system based on sparse coding and a formal concept hierarchy obtained from word embeddings. Our system took …...

Continuous representations for words or phrases, trained on large unlabeled corpora are proved very useful for many natural language processing tasks. While these vector representations capture many fine-grained syntactic and …...

Community Question Answering (CQA) websites have become a very popular and useful source of information, which helps users to find out answers to their corresponding questions. On one hand, if …...

AbstractAnimacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, …...

Large collections of texts are commonly generated by large organizations and making sense of these collections of texts is a significant challenge. One method for handling this is to organize …...

AbstractHuman communication includes information, opinions and reactions. Reactions are often captured by the affective-messages in written as well as verbal communications. While there has been work in affect modeling and …...

All-words word sense disambiguation (all-words WSD) is the task of identifying the senses of all words in a document. Since the sense of a word depends on the context, such …...

AbstractThis paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish valence tasks. Our system …...

AbstractCluster labeling is the assignment of representative labels to clusters obtained from the organization of a document collection. Once assigned, the labels can play an important role in applications such …...

An evaluation of distributed word representation is generally conducted using a word similarity task and/or a word analogy task. There are many datasets readily available for these tasks in English. …...

AbstractWord embeddings have become a mainstream tool in statistical natural language processing. Practitioners often use pre-trained word vectors, which were trained on large generic text corpora, and which are readily …...

AbstractThe paper describes our approach for SemEval-2018 Task 1: Affect Detection in Tweets. We perform experiments with manually compelled sentiment lexicons and word embeddings. We test their performance on twitter …...

AbstractWhile significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are …...

AbstractLexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity …...

AbstractWord embeddings are increasingly being used as a tool to study word associations in specific corpora. However, it is unclear whether such embeddings reflect enduring properties of language or if …...

Neural machine translation systems have been shown to achieve state-of-the-art translation per- formance for many language pairs. In order to produce a correct translation, MT systems must learn how to …...

AbstractWe introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for …...

A distributional semantics model — also known as word embeddings — is a major asset for any language as the research results reported in the literature have consistently shown that …...

AbstractDistributional word representations (often referred to as word embeddings) are omnipresent in modern NLP. Early work has focused on building representations for word types, and recent studies show that lemmatization …...

AbstractThis paper presents a comparison of several approaches for capturing discriminative attributes and considers an impact of concatenation of several word embeddings of different nature on the classification performance. A …...

AbstractRecently, a significant number of studies have focused on neural information retrieval (IR) models. One category of works use unlabeled data to train general word embeddings based on term proximity, …...

Methods for learning lower-dimensional representations (embeddings) of words using unlabelled data have received a renewed interested due to their myriad success in various Natural Language Processing (NLP) tasks. However, despite …...

AbstractMeasuring the salience of a word is an essential step in numerous NLP tasks. Heuristic approaches such as tfidf have been used so far to estimate the salience of words. …...

AbstractSpatial understanding is crucial in many real-world problems, yet little progress has been made towards building representations that capture spatial knowledge. Here, we move one step forward in this direction …...

AbstractWe investigate grounded sentence representations, where we train a sentence encoder to predict the image features of a given caption—i.e., we try to “imagine” how a sentence would be depicted …...

AbstractWe present LightRel, a lightweight and fast relation classifier. Our goal is to develop a high baseline for different relation extraction tasks. By defining only very few data-internal, word-level features …...

AbstractWord embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they represent when the word is polysemous. Here it is shown that multiple word senses reside …...

AbstractIn this paper we present the system submitted to the SemEval2018 task2 : Multilingual Emoji Prediction. Our system approaches both languages as being equal by first; considering word embeddings associated …...

AbstractSemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types. We tested 3 classes of …...

The paper presents a method for parsing low-resource languages with very small training corpora using multilingual word embeddings and annotated corpora of larger languages. The study demonstrates that specific language …...

AbstractNeural activation models have been proposed in the literature that use a set of example words for which fMRI measurements are available in order to find a mapping between word …...

AbstractThis paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were …...

AbstractDiscovering semantic relations within textual documents is a timely topic worthy of investigation. Natural language processing strategies are generally used for linking chunks of text in order to extract information …...

In recent years, the representation of words as vectors in a vector space, also known as word embeddings, has achieved a high degree of attention in the research community and …...

AbstractThere is a long-standing interest in understanding the internal behavior of neural networks. Deep neural architectures for natural language processing (NLP) are often accompanied by explanations for their effectiveness, from …...

AbstractWord embeddings obtained from neural network models such as Word2Vec Skipgram have become popular representations of word meaning and have been evaluated on a variety of word similarity and relatedness …...

AbstractGiven a set of instances of some relation, the relation induction task is to predict which other word pairs are likely to be related in the same way. While it …...

AbstractWe present SeVeN (Semantic Vector Networks), a hybrid resource that encodes relationships between words in the form of a graph. Different from traditional semantic networks, these relations are represented as …...

AbstractWe present a new summary evaluation approach that does not require human model summaries. Our approach exploits the compositional capabilities of corpus-based and lexical resource-based word embeddings to develop the …...

This paper discusses current trends in DeReKo, the German Reference Corpus, concerning legal issues around the recent German copyright reform with positive implications for corpus building and corpus linguistics in …...

AbstractWe present the system built for SemEval-2018 Task 2 on Emoji Prediction. Although Twitter messages are very short we managed to design a wide variety of features: textual, semantic, sentiment, …...

AbstractIn this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, …...

AbstractFollowing the recent success of word embeddings, it has been argued that there is no such thing as an ideal representation for words, as different models tend to capture divergent …...

AbstractWhen learning POS taggers and syntactic chunkers for low-resource languages, different resources may be available, and often all we have is a small tag dictionary, motivating type-constrained unsupervised induction. Even …...

AbstractIt has been demonstrated that vector-based representations of words trained on large text corpora encode linguistic regularities that may be exploited via the use of vector space arithmetic. This capability …...

AbstractThis paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection …...

AbstractRepresenting the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion …...

The acquisition of synonyms and quasi-synonyms of multi-word terms (MWTs) is a relatively new and under represented topic of research. However, dealing with MWT synonyms and semantically related terms is …...

AbstractPre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram …...

AbstractOnline texts - across genres, registers, domains, and styles - are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, perpetuate and amplify …...

AbstractAn interesting method of evaluating word representations is by how much they reflect the semantic representations in the human brain. However, most, if not all, previous works only focus on …...

AbstractHow do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine’s captain nuclear? These questions are easy for …...

AbstractThis paper describes our participation in the SemEval 2019 Task 3 - Contextual Emotion Detection in Text. This task aims to identify emotions, viz. happiness, anger, sadness in the context …...

AbstractCalculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS …...

AbstractRecent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating such bias only in …...

AbstractWord embeddings are now pervasive across NLP subfields as the de-facto method of forming text representataions. In this work, we show that existing embedding models are inadequate at constructing representations …...

AbstractIn low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy. Previous works have …...

AbstractWe present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information …...

AbstractThough languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which events change …...

AbstractThis paper describes our participation in the SemEval 2019 Task 5 - Multilingual Detection of Hate. This task aims to identify hate speech against two specific targets, immigrants and women. …...

AbstractWe present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 …...

AbstractIn countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as …...

AbstractMany natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun’s …...

AbstractWord embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While there have been some …...

AbstractInsufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen …...

AbstractThis paper treats gender bias latent in word embeddings. Previous mitigation attempts rely on the operationalisation of gender bias as a projection over a linear subspace. An alternative approach is …...

AbstractThis paper presents the Know-Center system submitted for task 5 of the SemEval-2019 workshop. Given a Twitter message in either English or Spanish, the task is to first detect whether …...

AbstractUser-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be …...

AbstractIn this paper we present a method to learn word embeddings that are resilient to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain a non-negligible amount …...

AbstractWord embeddings typically represent different meanings of a word in a single conflated vector. Empirical analysis of embeddings of ambiguous words is currently limited by the small size of manually …...

AbstractIn neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, …...

AbstractWhile word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this …...

AbstractWord embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution …...

AbstractSince deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks. Such high performance …...

AbstractTraining data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems. This means classifiers must generalize from limited …...

AbstractWord embeddings are established as very effective models used in several NLP applications. If they differ in their architecture and training process, they often exhibit similar properties and remain vector …...

AbstractIn the present paper we describe the UPV-28-UNITO system’s submission to the RumorEval 2019 shared task. The approach we applied for addressing both the subtasks of the contest exploits both …...

AbstractChinese is a logographic writing system, and the shape of Chinese characters contain rich syntactic and semantic information. In this paper, we propose a model to learn Chinese word embeddings …...

AbstractEnsuring smooth communication is essential in a chat-oriented dialogue system, so that a user can obtain meaningful responses through interactions with the system. Most prior work on dialogue research does …...

AbstractWe propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are …...

AbstractWe present a novel retrofitting model that can leverage relational knowledge available in a knowledge resource to improve word embeddings. The knowledge is captured in terms of relation inequality constraints …...

AbstractIn this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We show that the reproduction …...

AbstractWe propose a simple and effective method for incorporating word clusters into the Continuous Bag-of-Words (CBOW) model. Specifically, we propose to replace infrequent input and output words in CBOW model …...

AbstractDistributed representations of words have been an indispensable component for natural language processing (NLP) tasks. However, the large memory footprint of word embeddings makes it challenging to deploy NLP models …...

AbstractIn this paper we present the GerCo dataset of adjective-noun collocations for German, such as alter Freund ‘old friend’ and tiefe Liebe ‘deep love’. The annotation has been performed by …...

AbstractWhile pre-trained word embeddings have been shown to improve the performance of downstream tasks, many questions remain regarding their reliability: Do the same pre-trained word embeddings result in the best …...

AbstractVector space models of words have long been claimed to capture linguistic regularities as simple vector translations, but problems have been raised with this claim. We decompose and empirically analyze …...

AbstractResearchers have recently demonstrated that tying the neural weights between the input look-up table and the output classification layer can improve training and lower perplexity on sequence learning tasks such …...

AbstractModels with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution. We introduce Emix, a data augmentation method that uses interpolations …...

AbstractPrior works investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors …...

AbstractPopular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language. Here, we introduce the Python package comp-syn, which …...

This study conducted an experiment to com- pare the word embeddings of a compound word and a word in Japanese on the same vec- tor space using bilingual word embeddings. …...

AbstractWord embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations. While personalized embeddings can be useful to …...

AbstractWe study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another. In particular, we focus …...

AbstractWe present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has …...

AbstractRecent years have seen a growing interest within the natural language processing (NLP) community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing …...

AbstractWord embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Uncontextualized word embeddings are used in many NLP tasks today, especially in …...

AbstractDeduplication is the task of identifying near and exact duplicate data items in a collection. In this paper, we present a novel method for deduplication of scholarly documents. We develop …...

AbstractThis paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings represent-ing language ID, part of speech, and …...

AbstractAnalyses of word embeddings have primarily focused on semantic and syntactic properties. However, word embeddings have the potential to encode other properties as well. In this paper, we propose a …...

AbstractWe present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply …...

AbstractPrior work has determined domain similarity using text-based features of a corpus. However, when using pre-trained word embeddings, the underlying text corpus might not be accessible anymore. Therefore, we propose …...

AbstractThis work focuses on Portuguese Named Entity Recognition (NER) in the Geology domain. The only domain-specific dataset in the Portuguese language annotated for NER is the GeoCorpus. Our approach relies …...

AbstractThis paper introduces ENGLAWI, a large, versatile, XML-encoded machine-readable dictionary extracted from Wiktionary. ENGLAWI contains 752,769 articles encoding the full body of information included in Wiktionary: simple words, compounds and …...

AbstractWe present a novel, domain expert-controlled, replicable procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings. In particular, our method is …...

AbstractWe introduce an array of open and accessible tools to facilitate the use of the Icelandic Gigaword Corpus, in the field of Natural Language Processing as well as for students, …...

AbstractThe representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based …...

Word embeddings are used in various fields of natural language processing. The use of word embeddings and concept or word sense em- beddings demonstrated effectiveness in many tasks, such as …...

AbstractPre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification. However, most classification datasets assume a large amount labeled …...

AbstractIn a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also …...

AbstractWord-embeddings are vital components of Natural Language Processing (NLP) models and have been extensively explored. However, they consume a lot of memory which poses a challenge for edge deployment. Embedding …...

AbstractTransfer learning improves quality for low-resource machine translation, but it is unclear what exactly it transfers. We perform several ablation studies that limit information transfer, then measure the quality impact …...

AbstractIn this paper, we introduce NLP resources for 11 major Indian languages from two major language families. These resources include: (a) large-scale sentence-level monolingual corpora, (b) pre-trained word embeddings, (c) …...

AbstractWord embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.) depending on the notion of context defined at training time. …...

AbstractThe success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the …...

AbstractWord embeddings have been successfully trained in many languages. However, both intrinsic and extrinsic metrics are variable across languages, especially for languages that depart significantly from English in morphology and …...

AbstractA Mathematical Word Problem (MWP) differs from a general textual representation due to the fact that it is comprised of numerical quantities and units, in addition to text. Therefore, MWP …...

AbstractIn text processing, deep neural networks mostly use word embeddings as an input. Embeddings have to ensure that relations between words are reflected through distances in a high-dimensional numeric space. …...

This paper describes the first trial of neu- ral machine translation (NMT) from historical Japanese to contemporary Japanese. To com- pensate for the lack of parallel data, we used pre-trained …...

AbstractMeasuring the scholarly impact of a document without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which …...

AbstractRecent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various …...

AbstractThe ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models. We …...

AbstractWord embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious …...

AbstractIn this study, we investigate the use of Brown clustering for offensive language detection. Brown clustering has been shown to be of little use when the task involves distinguishing word …...

AbstractOut-of-vocabulary (oov) words cause serious troubles in solving natural language tasks with a neural network. Existing approaches to this problem resort to using subwords, which are shorter and more ambiguous …...

AbstractWe present SAPPHIRE, a Simple Aligner for Phrasal Paraphrase with HIerarchical REpresentation. Monolingual phrase alignment is a fundamental problem in natural language understanding and also a crucial technique in various …...

AbstractSocial media is a valuable data resource for various natural language processing (NLP) tasks. However, standard NLP tools were often designed with standard texts in mind, and their performance decreases …...

AbstractWe reduce the model size of pre-trained word embeddings by a factor of 200 while preserving its quality. Previous studies in this direction created a smaller word embedding model by …...

AbstractTopic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but …...

AbstractTopic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we …...

AbstractValency lexicons usually describe valency behavior of verbs in non-reflexive and non-reciprocal constructions. However, reflexive and reciprocal constructions are common morphosyntactic forms of verbs. Both of these constructions are characterized …...

AbstractWe investigate the hypothesis that translations can be used to identify cross-lingual lexical entailment. We propose novel methods that leverage parallel corpora, word embeddings, and multilingual lexical resources. Our results …...

AbstractWe present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contextualized embeddings for evidence of intersectional biases against …...

AbstractThe choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, …...

AbstractSocial biases are encoded in word embeddings. This presents a unique opportunity to study society historically and at scale, and a unique danger when embeddings are used in downstream applications. …...

AbstractWhat do people know when they know the meaning of words? Word associations have been widely used to tap into lexical repre- sentations and their structure, as a way of …...

AbstractLanguage Models have long been a prolific area of study in the field of Natural Language Processing (NLP). One of the newer kinds of language models, and some of the …...

AbstractConventional opinion polls were usually conducted via questionnaires or phone interviews, which are time-consuming and error-prone. With the advances in social networking platforms, it’s easier for the general public to …...

AbstractWord embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages. To gain further insight into word embeddings, we …...

AbstractFrom statistical to neural models, a wide variety of topic modelling algorithms have been proposed in the literature. However, because of the diversity of datasets and metrics, there have not …...

AbstractThis paper presents the system we submitted to the first Lexical Complexity Prediction (LCP) Shared Task 2021. The Shared Task provides participants with a new English dataset that includes context …...

For domain-specific NLP tasks, applying word embeddings trained on general cor- pora is not optimal. Meanwhile, training domain-specific word representations poses challenges to dataset construction and embed- ding evaluation. In …...

AbstractWe introduce a new approach for smoothing and improving the quality of word embeddings. We consider a method of fusing word embeddings that were trained on the same corpus but …...

AbstractWord embeddings trained on large corpora have shown to encode high levels of unfair discriminatory gender, racial, religious and ethnic biases. In contrast, human-written dictionaries describe the meanings of words …...

The field of natural language understanding has experienced exponential progress in the last few years, with impressive results in sev- eral tasks. This success has motivated re- searchers to study …...

AbstractNatural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations. To guide efforts at debiasing these …...

AbstractLanguage grounding aims at linking the symbolic representation of language (e.g., words) into the rich perceptual knowledge of the outside world. The general approach is to embed both textual and …...

AbstractWe present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features …...

Reporting and providing test sets for harm- ful bias in NLP applications is essential for building a robust understanding of the cur- rent problem. We present a new observation of …...

AbstractThough word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection …...

AbstractThe Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words …...

AbstractRecent researches have shown that large natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack. Backdoor attacked models can achieve good performance …...

AbstractIn writing, humor is mainly based on figurative language in which words and expressions change their conventional meaning to refer to something without saying it directly. This flip in the …...

AbstractTransfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that maximize parameter sharing trains …...

AbstractThe aim of vocabulary inventory prediction is to predict a learner’s whole vocabulary based on a limited sample of query words. This paper approaches the problem starting from the 2-parameter …...

AbstractThis paper describes the first publicly available corpus of Hmong, a minority language of China, Vietnam, Laos, Thailand, and various countries in Europe and the Americas. The corpus has been …...

AbstractDebiasing word embeddings has been largely limited to individual and independent social categories. However, real-world corpora typically present multiple social categories that possibly correlate or intersect with each other. For …...

AbstractA common paradigm for identifying semantic differences across social and temporal contexts is the use of static word embeddings and their distances. In particular, past work has compared embeddings against …...

AbstractWord embeddings learned using the distributional hypothesis (e.g., GloVe, Word2vec) are good at encoding various lexical-semantic relations. However, they do not capture the emotion aspects of words. We present a …...

AbstractThe introduction of word embedding models has remarkably changed many Natural Language Processing tasks. Word embeddings can automatically capture the semantics of words and other hidden features. Nonetheless, the Arabic …...

AbstractWord embeddings are critical for numerous NLP tasks but their evaluation in actual under-resourced settings needs further examination. This paper presents a case study in Bribri, a Chibchan language from …...

AbstractReducing the complexity of texts by applying an Automatic Text Simplification (ATS) system has been sparking interest inthe area of Natural Language Processing (NLP) for several years and a number …...

AbstractState-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words. To address this issue, we follow the principle of mimick-like models to …...

AbstractThe evaluation of recent embedding-based evaluation metrics for text generation is primarily based on measuring their correlation with human evaluations on standard benchmarks. However, these benchmarks are mostly from similar …...

AbstractWord embeddings (Mikolov et al., 2013; Pennington et al., 2014) have been used to bolster the performance of natural language processing systems in a wide variety of tasks, including information …...

AbstractCross-lingual alignment of word embeddings are important in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches relying on learning structure-preserving transformations, using adversarial …...

AbstractNamed Entity Recognition (NER), an essential subtask in NLP that identifies text belonging to predefined semantics such as a person, location, organization, drug, time, clinical procedure, biological protein, etc. NER …...

AbstractBilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, …...

AbstractSystematic Offensive stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social …...

AbstractThe manner in which gender is portrayed in materials used to teach children conveys messages about people’s roles in society. In this paper, we measure the gendered depiction of central …...

AbstractFeature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of …...

AbstractThe power of word embeddings is attributed to the linguistic theory that similar words will appear in similar contexts. This idea is specifically invoked by noting that “you shall know …...

The grammatical analysis of texts in any written language typically involves a num- ber of basic processing tasks, such as tok- enization, morphological tagging, and de- pendency parsing. State-of-the-art systems …...

AbstractDefinitions are a fundamental building block in lexicography, linguistics and computational semantics. In NLP, they have been used for retrofitting word embeddings or augmenting contextual representations in language models. However, …...

AbstractDense vector representations for textual data are crucial in modern NLP. Word embeddings and sentence embeddings estimated from raw texts are key in achieving state-of-the-art resultsin various tasks requiring semantic …...

Lexical complexity is crucial for reading comprehension. In the past, research work of lexical complexity prediction mainly focuses on differentiating the complexity difference between two words. Moreover, most of the …...

AbstractSentiment analysis has become a central tool in various disciplines outside of natural language processing. In particular in applied and domain-specific settings with strong requirements for interpretable methods, dictionary-based approaches …...

AbstractExisting bias mitigation methods require social-group-specific word pairs (e.g., “man” – “woman”) for each social attribute (e.g., gender), restricting the bias mitigation to only one specified social attribute. Further, this …...

AbstractUnsupervised anomaly detection seeks to identify deviant data samples in a dataset without using labels and constitutes a challenging task, particularly when the majority class is heterogeneous. This paper addresses …...