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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 401–450 of 4002 papers

TitleStatusHype
ASR error management for improving spoken language understanding—0
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media—0
Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance—0
A multi-level approach for hierarchical Ticket Classification—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP—0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations—0
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax—0
A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases—0
Associating Neural Word Embeddings With Deep Image Representations Using Fisher Vectors—0
Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics—0
A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments—0
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable—0
A Structured Distributional Model of Sentence Meaning and Processing—0
A Structured Distributional Semantic Model for Event Co-reference—0
A Structured Distributional Semantic Model : Integrating Structure with Semantics—0
A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT—0
A Study of Neural Matching Models for Cross-lingual IR—0
A study of semantic augmentation of word embeddings for extractive summarization—0
Analogical Proportions and Creativity: A Preliminary Study—0
A Deep Learning System for Automatic Extraction of Typological Linguistic Information from Descriptive Grammars—0
ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.—0
A Minimalist Approach to Shallow Discourse Parsing and Implicit Relation Recognition—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
A survey of cross-lingual features for zero-shot cross-lingual semantic parsing—0
A Survey Of Cross-lingual Word Embedding Models—0
A Word-Embedding-based Sense Index for Regular Polysemy Representation—0
A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches—0
A comparative analysis of embedding models for patent similarity—0
A Survey On Neural Word Embeddings—0
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings—0
A Survey on Word Meta-Embedding Learning—0
A Syllable-based Technique for Word Embeddings of Korean Words—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
Massively Multilingual Lexical Specialization of Multilingual Transformers—0
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning—0
Analysis of Italian Word Embeddings—0
A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations—0
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification—0
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
Analysis of Word Embeddings Using Fuzzy Clustering—0
A Topical Approach to Capturing Customer Insight In Social Media—0
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings—0
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation—0
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks—0
Attending Sentences to detect Satirical Fake News—0
Attending to Characters in Neural Sequence Labeling Models—0
Attention-based model for predicting question relatedness on Stack Overflow—0
Attention-based Semantic Priming for Slot-filling—0
A Methodology for Studying Linguistic and Cultural Change in China, 1900-1950—0
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