Word2Vec

We examine the embedding approach to reason new relational facts from a large- scale knowledge graph and a text corpus. We propose a novel method of jointly em- bedding entities …...

Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)- based methods. However some mod- els are opaque to probabilistic interpre- tation, and MF-based …...

We present a novel word level vector rep- resentation based on symmetric patterns (SPs). For this aim we automatically ac- quire SPs (e.g., “X and Y”) from a large corpus …...

We present two simple modifications to the models in the popular Word2Vec tool, in or- der to generate embeddings more suited to tasks involving syntax. The main issue with the …...

Sparse representations of text such as bag-of- words models or extended explicit semantic analysis (ESA) representations are commonly used in many NLP applications. However, for short texts, the similarity between …...

This paper presents our participation in SemEval-2015 task 12 (Aspect Based Senti- ment Analysis). We participated employing only unsupervised or weakly-supervised ap- proaches. Our attempt is based on requiring the …...

Distributional semantics creates vector- space representations that capture many forms of semantic similarity, but their re- lation to semantic entailment has been less clear. We propose a vector-space model which …...

AbstractWe present Ambient Search, an open source system for displaying and retrieving relevant documents in real time for speech input. The system works ambiently, that is, it unobstructively listens to …...

Semantic Textual Similarity measures similar- ity between pair of texts, even though the sim- ilar context is projected using different words. This work attempted to incorporate the con- text space …...

This paper describes our participation in the SemEval-2016 Task 1: Semantic Textual Sim- ilarity (STS). We developed three methods for the English subtask (STS Core). The first method is unsupervised …...

AbstractMonitoring social media has been shown to be an interesting approach for the early detection of drug adverse effects. In this paper, we describe a system which extracts medical entities …...

AbstractIn this paper, we outline an approach to build graph-based reverse dictionaries using word definitions. A reverse dictionary takes a phrase as an input and outputs a list of words …...

In this paper we present our system de- veloped for the SemEval 2016 Task 2 - Interpretable Semantic Textual Similarity along with the results obtained for our sub- mitted runs. …...

We describe a technique for adding contex- tual distinctions to word embeddings by ex- tending the usual embedding process — into two phases. The first phase resembles existing methods, but …...

AbstractIn recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most …...

AbstractRecognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its …...

AbstractWe present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes word2vec’s features, paragraph vector (batch and online) …...

Distributional semantic models (DSMs) are often evaluated on artificial simi- larity datasets containing single words or fully compositional phrases. We present a large-scale multilingual eval- uation of DSMs for predicting …...

Resorting to community question answer- ing (CQA) websites for finding answers has gained momentum in the past decade with the explosive rate at which social media has been proliferating. With …...

Regularization is a critical step in supervised learning to not only address overfitting, but also to take into account any prior knowledge we may have on the features and their …...

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 …...

This paper describes our sentiment clas- sification system for microblog-sized doc- uments, and documents where a topic is present. The system consists of a soft- voting ensemble of a word2vec …...

In this paper we describe our participa- tion in the STS Core subtask which is the determination of the monolingual seman- tic similarity between pair of sentences. In our participation …...

AbstractIn this study, we introduce a new approach for learning language models by training them to estimate word-context pointwise mutual information (PMI), and then deriving the desired conditional probabilities from …...

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 …...

AbstractDistributional semantics models are known to struggle with small data. It is generally accepted that in order to learn ‘a good vector’ for a word, a model must have sufficient …...

AbstractThis paper describes our system, entitled Idiom Savant, for the 7th Task of the Semeval 2017 workshop, “Detection and interpretation of English Puns”. Our system consists of two probabilistic models …...

AbstractEntities are essential elements of natural language. In this paper, we present methods for learning multi-level representations of entities on three complementary levels: character (character patterns in entity names extracted, …...

AbstractThis paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., …...

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 …...

AbstractIdentification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis. For example, ‘master’, ‘seasoned’ and ‘familiar’ point to …...

AbstractThis document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B - English, determining the polarity towards a topic on …...

AbstractThis paper explores the role of semantic relatedness features, such as word associations, in humour recognition. Specifically, we examine the task of inferring pairwise humour judgments in Twitter hashtag wars. …...

This paper describes our submission to the WMT 2017 Neural MT Training Task. We modified the provided NMT system in order to allow for interrupting and con- tinuing the training …...

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 …...

We started to construct a database of synonymous expressions of Japanese “Verb + Verb” compounds semi-automatically. Japanese is known to be rich in compound verbs consisting of two verbs joined …...

AbstractDistributed representations of words learned from text have proved to be successful in various natural language processing tasks in recent times. While some methods represent words as vectors computed from …...

AbstractPrepositions are among the most frequent words in English and play complex roles in the syntax and semantics of sentences. Not surprisingly, they pose well-known difficulties in automatic processing of …...

Croatian is poorly resourced and highly inflected language from Slavic language family. Nowadays, research is focusing mostly on English. We created a new word analogy dataset based on the original …...

AbstractThis paper discusses the experiments performed for predicting the emotion intensity in tweets using a generalized supervised learning approach. We extract 3 kind of features from each of the tweets …...

We present a novel, minimally supervised method of generating word embedding evaluation datasets for a large number of languages. Our approach utilizes existing dependency treebanks and parsers in order to …...

AbstractNeural word embeddings models (such as those built with word2vec) are known to have stability problems: when retraining a model with the exact same hyperparameters, words neighborhoods may change. We …...

AbstractWords are polysemous and multi-faceted, with many shades of meanings. We suggest that sparse distributed representations are more suitable than other, commonly used, (dense) representations to express these multiple facets, …...

Multimodal representations are distributed vectors that map multiple modes of information to a single mathematical space, where distances between instances delineate their similarity. In most cases, using a single unimodal …...

AbstractIn this paper, I describe a fusion model combining contextualized and static word representations for approaching the EmoContext task in the SemEval 2019 competition. The model is based on two …...

AbstractThe ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other …...

AbstractThis paper discusses the solution to the problem statement of the SemEval19: EmoContext competition which is ”Contextual Emotion Detection in Texts”. The paper includes the explanation of an architecture that …...

AbstractTask 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes …...

AbstractThe paper presents an evaluation of word embedding models in clustering of texts in the Polish language. Authors verified six different embedding models, starting from widely used word2vec, across fastText …...

AbstractThis paper describes our system submissions as part of our participation (team name: JU_ETCE_17_21) in the SemEval 2019 shared task 6: “OffensEval: Identifying and Catego- rizing Offensive Language in Social …...

AbstractWe build the first full pipeline for semantic role labelling of Russian texts. The pipeline implements predicate identification, argument extraction, argument classification (labeling), and global scoring via integer linear programming. …...

AbstractA standard word embedding algorithm, such as word2vec and glove, makes a strong assumption that words are likely to be semantically related only if they co-occur locally within a window …...

AbstractIn this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums. We use Word2Vec to learn the distributed …...

AbstractAutomatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection …...

AbstractSemEval-2020 Task 1 is devoted to detection of changes in word meaning over time. The first subtask raises a question if a particular word has acquired or lost any of …...

AbstractIn the paper, we present class-based LSTM Russian language models (LMs) with classes generated with the use of both word frequency and linguistic information data, obtained with the help of …...

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 …...

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 …...

AbstractThis paper describes our team work and submission for the SemEval 2020 (Sub-Task A) “Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media”. Our two baseline models …...

AbstractSince word embeddings have been the most popular input for many NLP tasks, evaluating their quality is critical. Most research efforts are focusing on English word embeddings. This paper addresses …...

AbstractThis work introduces additions to the corpus ChoCo, a multimodal corpus for the American indigenous language Choctaw. Using texts from the corpus, we develop new computational resources by using two …...

AbstractContextualized representations (e.g. ELMo, BERT) have become the default pretrained representations for downstream NLP applications. In some settings, this transition has rendered their static embedding predecessors (e.g. Word2Vec, GloVe) obsolete. …...

AbstractUnsupervised Machine Translation has been advancing our ability to translate without parallel data, but state-of-the-art methods assume an abundance of monolingual data. This paper investigates the scenario where monolingual data …...

AbstractTraditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. A retrofitting procedure may be needed to solve this …...

AbstractCreating ontologies is an expensive task. Our vision is that we can automatically generate ontologies based on a set of relevant documents to create a kick-start in ontology creating sessions. …...

AbstractPredicting the degree of compositionality of noun compounds such as “snowball” and “butterfly” is a crucial ingredient for lexicography and Natural Language Processing applications, to know whether the compound should …...

AbstractThis paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and …...

AbstractWith the rapid growth in technology, social media activity has seen a boom across all age groups. It is humanly impossible to check all the tweets, comments and status manually …...

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 …...

AbstractAs the system of confiscation becomes more and more perfect, grasping the distribution of the types of confiscations actually announced by the courts will enable you to understand changing of …...

AbstractIdentifying intertextual relationships between authors is of central importance to the study of literature. We report an empirical analysis of intertextuality in classical Latin literature using word embedding models. To …...

AbstractSentiment analysis has become a popular research issue in recent years, especially on educational texts which is an important problem. According to literature, the similar sentence generation can help the …...

AbstractHate and offensive speech on social media is targeted to attack an individual or group of community based on protected characteristics such as gender, ethnicity, and religion. Hate and offensive …...

AbstractSelf-supervised learning (SSL) methods such as Word2vec, BERT, and GPT have shown great effectiveness in language understanding. Contrastive learning, as a recent SSL approach, has attracted increasing attention in NLP. …...

AbstractThe paper aims to identify cited text spans in the reference paper related to the given citance in the citing paper. We refer to it as cited text span retrieval …...

AbstractSocial media is the ultimate challenge for many natural language processing tools. The constant emergence of linguistic constructs challenge even the most sophisticated NLP tools. Predicting word embeddings for out …...

AbstractExtracting relevant user behaviors through customer’s transaction description is one of the ways to collect customer information. In the current text mining field, most of the researches are mainly study …...

In this study, we propose a method to construct Japanese BERT using fixed Token Embedding to reduce BERT model construction time. This method constructs word embeddings in advance using word2vec …...

AbstractThe paper presents novel resources and experiments for Buddhist Sanskrit, broadly defined here including all the varieties of Sanskrit in which Buddhist texts have been transmitted. We release a novel …...

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 …...

AbstractUnderstanding child language development requires accurately representing children’s lexicons. However, much of the past work modeling children’s vocabulary development has utilized adult-based measures. The present investigation asks whether using corpora …...

This paper introduces the Forgotten Realms Wiki (FRW) data set and domain specific natu- ral language generation using FRW along with related analyses. Forgotten Realms is the de- facto default …...

AbstractStance detection is the task of automatically eliciting stance information towards a specific claim made by a primary author. While most studies have been done for high-resource languages, this work …...

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 …...

AbstractWord sense disambiguation is an NLP task embedded in different applications. We propose to evaluate its contribution to the automatic translation of French texts into pictographs, in the context of …...