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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 3151–3200 of 4002 papers

TitleStatusHype
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA—0
Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings—0
A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task—0
YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter—0
Risk Bounds for Transferring Representations With and Without Fine-Tuning—0
Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task—0
Metaphor Detection in a Poetry Corpus—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
Clinical Event Detection with Hybrid Neural Architecture—0
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words—0
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding—0
BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches—0
Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible—0
Learning Word Representations with Regularization from Prior Knowledge—0
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text—0
TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers—0
LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity—0
Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention—0
PKU\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge—0
TurkuNLP: Delexicalized Pre-training of Word Embeddings for Dependency Parsing—0
Multi-Model and Crosslingual Dependency Analysis—0
Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter—0
TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification—0
Automated Preamble Detection in Dictated Medical Reports—0
Event Detection Using Frame-Semantic Parser—0
UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise FeaturesCode0
Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity—0
UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA—0
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base—0
BB\_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs—0
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings—0
Multilingual Semantic Parsing And Code-SwitchingCode0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders—0
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings—0
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language—0
MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications—0
Comparing Approaches for Automatic Question Identification—0
HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment—0
An Ontology-Based Method for Extracting and Classifying Domain-Specific Compositional Nominal Compounds—0
Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity—0
Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits—0
ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and Interpretation—0
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity—0
L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity—0
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news—0
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