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Distributed word representations have been widely used and proven to be useful in quite a few natural language processing and text mining tasks. Most of existing word embedding models aim …...

AbstractWe introduce two document-level features to polish baseline sentence-level translations generated by a state-of-the-art statistical machine translation (SMT) system. One feature uses the word-embedding technique to model the relation between …...

Sponsored search is at the center of a multibil- lion dollar market established by search tech- nology. Accurate ad click prediction is a key component for this market to function …...

Word embedding has been found to be high- ly powerful to translate words from one lan- guage to another by a simple linear transfor- m. However, we found some inconsistence …...

Some state-of-the-art summarization systems use integer linear programming (ILP) based methods that aim to maximize the important concepts covered in the summary. These con- cepts are often obtained by selecting …...

In the last two years, there has been a surge of word embedding algorithms and research on them. However, evaluation has mostly been carried out on a narrow set of …...

AbstractWe propose a novel neural lemmatization model which is language independent and supervised in nature. To handle the words in a neural framework, word embedding technique is used to represent …...

Understanding unstructured text is a ma- jor goal within natural language process- ing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we …...

AbstractNowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model …...

AbstractWe propose a novel word embedding-based hypernym generation model that jointly learns clusters of hyponym-hypernym relations, i.e., hypernymy, and projections from hyponym to hypernym embeddings. Most of the recent hypernym …...

This paper presents our submissions for se- mantic textual similarity task in SemEval 2016. Based on several traditional fea- tures (i.e., string-based, corpus-based, ma- chine translation similarity and alignmen- t …...

Word embedding maps words into a low- dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps …...

In this work, we apply classifier fusion to tweet polarity identification problem. The task is to predict whether the emotion hidden in a tweet is positive, neutral, or negative. An …...

AbstractIn the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications. Learning representations of words is …...

In this work, we focus on the problem of news citation recommendation. The task aims to recommend news citations for both authors and readers to create and search news references. …...

AbstractNamed-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of …...

Word embedding models learn vectorial word representations that can be used in a variety of NLP applications. When training data is scarce, these models risk losing their gener- alization abilities …...

Verbal comprehension questions appear very frequently in Intelligence Quotient (IQ) tests, which measure human’s verbal ability includ- ing the understanding of the words with mul- tiple senses, the synonyms and …...

AbstractBilingual lexica are the basis for many cross-lingual natural language processing tasks. Recent works have shown success in learning bilingual dictionary by taking advantages of comparable corpora and a diverse …...

Embedding words in a vector space has gained a lot of attention in recent years. While state- of-the-art methods provide efficient computa- tion of word similarities via a low-dimensional matrix …...

AbstractIntegrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention …...

AbstractCross-lingual natural language processing hinges on the premise that there exists invariance across languages. At the word level, researchers have identified such invariance in the word embedding semantic spaces of …...

AbstractText mining has drawn significant attention in recent past due to the rapid growth in biomedical and clinical records. Entity extraction is one of the fundamental components for biomedical text …...

AbstractUser generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on …...

AbstractSince the advent of word embedding methods, the representation of longer pieces of texts such as sentences and paragraphs is gaining more and more interest, especially for textual similarity tasks. …...

AbstractRecently, neural twitter sentiment classification has become one of state-of-thearts, which relies less feature engineering work compared with traditional methods. In this paper, we propose a simple and effective ensemble …...

AbstractThis paper presents a new, efficient method for learning task-specific word vectors using a variant of the Passive-Aggressive algorithm. Specifically, this algorithm learns a word embedding matrix in tandem with …...

AbstractNon-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique. We show that weighting methods such as positive pointwise mutual information …...

AbstractIn recent years word-embedding models have gained great popularity due to their remarkable performance on several tasks, including word analogy questions and caption generation. An unexpected “side-effect” of such models …...

AbstractIn this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach supports language variants and dialects with very limited parallel …...

AbstractA number of machine translation methods have been proposed in recent years to deal with the increasingly important problem of automatic translation between texts of different languages or languages and …...

AbstractThis paper describes the XJSA System submission from XJTU. Our system was created for SemEval2017 Task 4 – subtask A which is very popular and fundamental. The system is based …...

AbstractIn this paper, we first utilize the word embedding that focuses on sub-word units to the Mongolian Phrase Break (PB) prediction task by using Long-Short-Term-Memory (LSTM) model. Mongolian is an …...

AbstractThis paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word’s similarity with …...

Sentiment classification techniques have been widely used for analyzing user opinions. In conventional supervised learning methods, hand-crafted features are needed, which requires a thorough understanding of the domain. Since social …...

Aspect identification in user generated texts by supervised text classification might suffer degradation in performance when changing to other domains than the one used for training. For referring to aspects …...

AbstractSimple reference games are of central theoretical and empirical importance in the study of situated language use. Although language provides rich, compositional truth-conditional semantics to facilitate reference, speakers and listeners …...

AbstractWe introduce a method for embedding words as probability densities in a low-dimensional space. Rather than assuming that a word embedding is fixed across the entire text collection, as in …...

AbstractWith the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly. However, many natural language processing tasks need sense level representation. To …...

Learning word vectors from character level is an effective method to improve word embeddings for morphologically rich languages. However, most of these techniques have been applied to languages that are …...

In recent years word embedding/distributional semantic models evolved to become a fundamental component in many natural language processing (NLP) architectures due to their ability of capturing and quantifying semantic associations …...

AbstractThis paper describes our approach to SemEval-2018 Task1: Estimation of Affects in Tweet for 1a and 2a. Our team KDE-AFFECT employs several methods including one-dimensional Convolutional Neural Network for n-grams, …...

AbstractWord embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word …...

AbstractHypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus. This paper proposes a simple but effective method for the discovery of hypernym sets …...

In this paper, we describe a sentiment analysis system implemented for the semantic-evaluation task of message polarity classification for English on Twitter. Our system contains modules of data pre-processing, word …...

AbstractStandard word embedding algorithms learn vector representations from large corpora of text documents in an unsupervised fashion. However, the quality of word embeddings learned from these algorithms is affected by …...

AbstractRepresentation learning is the foundation of machine reading comprehension. In state-of-the-art models, deep learning methods broadly use word and character level representations. However, character is not naturally the minimal linguistic …...

AbstractWord embedding models have gained a lot of traction in the Natural Language Processing community, however, they suffer from unintended demographic biases. Most approaches to evaluate these biases rely on …...

AbstractThis paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. Two …...

AbstractGiven many recent advanced embedding models, selecting pre-trained word representation (i.e., word embedding) models best fit for a specific downstream NLP task is non-trivial. In this paper, we propose a …...

AbstractWord embedding is central to neural machine translation (NMT), which has attracted intensive research interest in recent years. In NMT, the source embedding plays the role of the entrance while …...

AbstractThere is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, …...

AbstractWord embedding models typically learn two types of vectors: target word vectors and context word vectors. These vectors are normally learned such that they are predictive of some word co-occurrence …...

AbstractMany researchers have tried to predict the accuracies of extrinsic evaluation by using intrinsic evaluation to evaluate word embedding. The relationship between intrinsic and extrinsic evaluation, however, has only been …...

AbstractWord embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns …...

AbstractElementary Discourse Unit (EDU) recognition is the basic task of discourse analysis, and the Chinese and English discourse alignment corpus is helpful to the studies of EDU recognition. This paper …...

AbstractIn this paper, we introduce a new set of lexicons for expressing subjectivity in text documents written in Brazilian Portuguese. Besides the non-English idiom, in contrast to other subjectivity lexicons …...

AbstractEarly intervention for suicide risks with social media data has increasingly received great attention. Using a suicide dictionary created by mental health experts is one of the effective ways to …...

AbstractWord embedding-based similarity measures are currently among the top-performing methods on unsupervised semantic textual similarity (STS) tasks. Recent work has increasingly adopted a statistical view on these embeddings, with some …...

AbstractIn order to access indigenous, regional knowledge contained in language corpora, semantic tools and network methods are most typically employed. In this paper we present an approach for the identification …...

AbstractEmbedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem. A popular solution is to train a character-level neural network to reproduce the embeddings from a standard …...

AbstractWord embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the problem of how to …...

AbstractDialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation data are biased. …...

AbstractNamed entity recognition is a key component in various natural language processing systems, and neural architectures provide significant improvements over conventional approaches. Regardless of different word embedding and hidden layer …...

AbstractWeb-users produce and publish high volumes of data of various types, such as text, images, and videos. The platforms try to restrain their users from publishing offensive content to keep …...

AbstractIn this paper we describe our work on the development and enrichment of OFrLex, a freely available, large-coverage morphological and syntactic Old French lexicon. We rely on several heterogeneous language …...

AbstractWith growing applications of Machine Learning in daily lives Natural Language Processing (NLP) has emerged as a heavily researched area. Finding its applications in tasks ranging from simple Q/A chatbots …...

AbstractSpeech translation (ST) aims to learn transformations from speech in the source language to the text in the target language. Previous works show that multitask learning improves the ST performance, …...

AbstractIn this study, we propose a multi-granularity ordinal classification method to address the problem of emphasis selection. In detail, the word embedding is learned from Embeddings from Language Model (ELMO) …...

AbstractBengali is a low-resource language that lacks tools and resources for profane and obscene textual content detection. Until now, no lexicon exists for detecting obscenity in Bengali social media text. …...

AbstractRelation extraction is a subtask of natural langage processing that has seen many improvements in recent years, with the advent of complex pre-trained architectures. Many of these state-of-the-art approaches are …...

AbstractThe Internet is home to thousands of communities, each with their own unique worldview and associated ideological differences. With new communities constantly emerging and serving as ideological birthplaces, battlegrounds, and …...

AbstractThe advancement of the web and information technology has contributed to the rapid growth of digital libraries and automatic machine translation tools which easily translate texts from one language into …...

AbstractWord embedding is considered an essential factor in improving the performance of various Natural Language Processing (NLP) models. However, it is hardly applicable in real-world datasets as word embedding is …...

AbstractWord embedding techniques depend heavily on the frequencies of words in the corpus, and are negatively impacted by failures in providing reliable representations for low-frequency words or unseen words during …...

AbstractLanguages are dynamic systems: word usage may change over time, reflecting various societal factors. However, all languages do not evolve identically: the impact of an event, the influence of a …...

AbstractDeep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction. In many cases, malicious inputs intentionally crafted for one model can …...

AbstractRecent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be …...

AbstractMetaphor detection plays an important role in tasks such as machine translation and human-machine dialogue. As more users express their opinions on products or other topics on socialmedia through metaphorical …...

AbstractPretrained language models (PLMs) have been shown to exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biases in language technologies. However, PLMs’ biases against people …...

AbstractSocial media platforms and online streaming services have spawned a new breed of Hate Speech (HS). Due to the massive amount of user-generated content on these sites, modern machine learning …...

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 …...

AbstractEnhancing the interpretability of text classification models can help increase the reliability of these models in real-world applications. Currently, most researchers focus on extracting task-specific words from inputs to improve …...

AbstractIn this work, we explore the fitness of various word/concept representations in analyzing an experimental verbal fluency dataset providing human responses to 10 different category enumeration tasks. Based on human …...

AbstractWith the necessity of privacy protection, it becomes increasingly vital to train deep neural models in a federated learning manner for natural language processing (NLP) tasks. However, recent studies show …...

AbstractThe ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces—their degree of “isomorphism.” We address the root-cause of faulty …...

AbstractLearning representations of words in a continuous space is perhaps the most fundamental task in NLP, however words interact in ways much richer than vector dot product similarity can provide. …...

AbstractIntimacy is one of the fundamental aspects of our social life. It relates to intimate interactions with others, often including verbal self-disclosure. In this paper, we researched machine learning algorithms …...

AbstractIn this paper, we propose Vocab-Expander at https://vocab-expander.com, an online tool that enables end-users (e.g., technology scouts) to create and expand a vocabulary of their domain of interest. It utilizes …...