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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 3051–3100 of 4002 papers

TitleStatusHype
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets—0
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity—0
C-3MA: Tartu-Riga-Zurich Translation Systems for WMT17Code0
LCT-MALTA's Submission to RepEval 2017 Shared Task—0
YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model—0
Recognizing Textual Entailment in Twitter Using Word Embeddings—0
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks v2.0—0
Adapting Neural Machine Translation with Parallel Synthetic Data—0
Investigating neural architectures for short answer scoring—0
Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets—0
Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation—0
Bilexical Embeddings for Quality Estimation—0
Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes—0
Lexicalized vs. Delexicalized Parsing in Low-Resource Scenarios—0
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings—0
Variable Mini-Batch Sizing and Pre-Trained Embeddings—0
Neural Networks and Spelling Features for Native Language Identification—0
Detecting Sarcasm Using Different Forms Of Incongruity—0
Predicting Pronouns with a Convolutional Network and an N-gram Model—0
LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination—0
Improving neural tagging with lexical information—0
Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases—0
Automatic Community Creation for Abstractive Spoken Conversations Summarization—0
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks—0
Prepositional Phrase Attachment over Word Embedding Products—0
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features—0
MEANT 2.0: Accurate semantic MT evaluation for any output language—0
The strange geometry of skip-gram with negative sampling—0
A Question Answering Approach for Emotion Cause Extraction—0
Deriving continous grounded meaning representations from referentially structured multimodal contexts—0
Refining Word Embeddings for Sentiment Analysis—0
VecShare: A Framework for Sharing Word Representation Vectors—0
Identifying attack and support argumentative relations using deep learning—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
Dict2vec : Learning Word Embeddings using Lexical DictionariesCode0
Adapting Topic Models using Lexical Associations with Tree Priors—0
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
Deeper Attention to Abusive User Content Moderation—0
Word Re-Embedding via Manifold Dimensionality Retention—0
Earth Mover's Distance Minimization for Unsupervised Bilingual Lexicon Induction—0
Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment SupervisionCode0
Towards a Universal Sentiment Classifier in Multiple languages—0
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis—0
Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model—0
Word-Context Character Embeddings for Chinese Word Segmentation—0
Exploiting Morphological Regularities in Distributional Word Representations—0
Predicting Word Association Strengths—0
Distinguishing Japanese Non-standard Usages from Standard Ones—0
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