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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 1901–1950 of 4002 papers

TitleStatusHype
Initial Experiments in Data-Driven Morphological Analysis for Finnish—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Deriving continous grounded meaning representations from referentially structured multimodal contexts—0
Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings—0
It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution—0
Jabberwocky Parsing: Dependency Parsing with Lexical Noise—0
Derivational Morphological Relations in Word Embeddings—0
Derivational Morphological Relations in Word Embeddings—0
Cross-lingual Word Embeddings beyond Zero-shot Machine Translation—0
Inspecting the concept knowledge graph encoded by modern language models—0
Instantiation—0
Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition—0
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting—0
Integrating Pause Information with Word Embeddings in Language Models for Alzheimer's Disease Detection from Spontaneous Speech—0
Integrating Reviews into Personalized Ranking for Cold Start Recommendation—0
Cross-lingual Word Embeddings in Hyperbolic Space—0
BERT-based Ranking for Biomedical Entity Normalization—0
Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach—0
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs—0
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping—0
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media—0
Automated Preamble Detection in Dictated Medical Reports—0
Interactive Re-Fitting as a Technique for Improving Word Embeddings—0
Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies—0
Interdependencies of Gender and Race in Contextualized Word Embeddings—0
Interpretable Adversarial Training for Text—0
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts—0
Interpretable embeddings to understand computing careers—0
Interpretable Neural Embeddings with Sparse Self-Representation—0
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation—0
Interpretable Word Embedding Contextualization—0
Interpretable Word Embeddings via Informative Priors—0
Interpreting Emoji with Emoji—0
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
Intrinsic analysis for dual word embedding space models—0
Intrinsic Bias Metrics Do Not Correlate with Application Bias—0
Intrinsic Evaluations of Word Embeddings: What Can We Do Better?—0
Intrinsic Image Captioning Evaluation—0
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks—0
Automatically Building a Multilingual Lexicon of False Friends With No Supervision—0
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili—0
Invariance and identifiability issues for word embeddings—0
InvBERT: Reconstructing Text from Contextualized Word Embeddings by inverting the BERT pipeline—0
A Neural Model for Compositional Word Embeddings and Sentence Processing—0
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