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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 3026–3050 of 4002 papers

TitleStatusHype
Think Globally, Embed Locally --- Locally Linear Meta-embedding of WordsCode0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
MetaLDA: a Topic Model that Efficiently Incorporates Meta informationCode0
Leveraging Distributional Semantics for Multi-Label Learning—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Affective Neural Response Generation—0
StarSpace: Embed All The Things!Code0
Social Media Text Processing and Semantic Analysis for Smart Cities—0
Improving average ranking precision in user searches for biomedical research datasets—0
Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings—0
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a DiscourseCode0
Using k-way Co-occurrences for Learning Word Embeddings—0
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment—0
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings—0
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects—0
Learning Neural Word Salience ScoresCode0
Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging—0
MappSent: a Textual Mapping Approach for Question-to-Question Similarity—0
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity—0
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets—0
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics—0
Neural Networks and Spelling Features for Native Language Identification—0
Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games—0
Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach—0
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