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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 1776–1800 of 4002 papers

TitleStatusHype
Exploration des relations sémantiques sous-jacentes aux plongements contextuels de mots (Exploring semantic relations underlying contextual word embeddings)—0
Clustering Prominent People and Organizations in Topic-Specific Text Corpora—0
ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic—0
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion—0
Identifying Aggression and Toxicity in Comments using Capsule Network—0
Identifying and interpreting non-aligned human conceptual representations using language modeling—0
Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings—0
Identifying attack and support argumentative relations using deep learning—0
Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings—0
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
Clustering of Russian Adjective-Noun Constructions using Word Embeddings—0
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes—0
Exploiting Morphological Regularities in Distributional Word Representations—0
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings—0
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features—0
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text—0
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search—0
Image Captioning using Deep Stacked LSTMs, Contextual Word Embeddings and Data Augmentation—0
Image Captioning with Visual Object Representations Grounded in the Textual Modality—0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
Argument from Old Man’s View: Assessing Social Bias in Argumentation—0
Impart Contextualization to Static Word Embeddings through Semantic Relations—0
A Margin-based Loss with Synthetic Negative Samples for Continuous-output Machine Translation—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
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