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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 1401–1450 of 4002 papers

TitleStatusHype
Evaluating Metrics for Bias in Word Embeddings—0
Evaluating Monolingual and Crosslingual Embeddings on Datasets of Word Association Norms—0
Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches—0
Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation—0
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks—0
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View—0
Detecting Paraphrases of Standard Clause Titles in Insurance Contracts—0
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification—0
Chinese Hypernym-Hyponym Extraction from User Generated Categories—0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
Chinese Zero Pronoun Resolution with Deep Neural Networks—0
Evaluating Sub-word Embeddings in Cross-lingual Models—0
Evaluating the Consistency of Word Embeddings from Small Data—0
Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi'kmaq Language Modelling—0
Detecting Most Frequent Sense using Word Embeddings and BabelNet—0
Evaluating the timing and magnitude of semantic change in diachronic word embedding models—0
Detecting Metaphorical Phrases in the Polish Language—0
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings—0
Evaluating vector-space models of analogy—0
Evaluating Word Embedding Hyper-Parameters for Similarity and Analogy Tasks—0
Evaluating Word Embedding Models: Methods and Experimental Results—0
Evaluating Word Embeddings for Indonesian--English Code-Mixed Text Based on Synthetic Data—0
Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings—0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri—0
CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM—0
Evaluating Word Embeddings on Low-Resource Languages—0
Evaluating Word Embeddings Using a Representative Suite of Practical Tasks—0
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings—0
Evaluating word embeddings with fMRI and eye-tracking—0
Evaluation Framework for Understanding Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms—0
Evaluation methods for unsupervised word embeddings—0
Evaluation of acoustic word embeddings—0
Classification Attention for Chinese NER—0
Evaluation of Deep Learning Models for Hostility Detection in Hindi Text—0
Word Embedding based New Corpus for Low-resourced Language: Sindhi—0
Evaluation of Domain-specific Word Embeddings using Knowledge Resources—0
Evaluation of Greek Word Embeddings—0
Evaluation of Morphological Embeddings for the Russian Language—0
Evaluation of Question Answering Systems: Complexity of judging a natural language—0
Classification of Micro-Texts Using Sub-Word Embeddings—0
Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC—0
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions—0
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks—0
Evaluation of Word Embeddings for the Social Sciences—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
A LSTM Approach with Sub-Word Embeddings for Mongolian Phrase Break Prediction—0
Event Detection Using Frame-Semantic Parser—0
Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language—0
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