Word Embeddings

777 papers with code • 0 benchmarks • 45 datasets

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.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Greatest papers with code

Adversarial Training Methods for Semi-Supervised Text Classification

tensorflow/models 25 May 2016

Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting.

General Classification Semi Supervised Text Classification +3

FastText.zip: Compressing text classification models

facebookresearch/fastText 12 Dec 2016

We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory.

General Classification Quantization +2

Enriching Word Vectors with Subword Information

facebookresearch/fastText TACL 2017

A vector representation is associated to each character $n$-gram; words being represented as the sum of these representations.

Word Embeddings Word Similarity

Toward Better Storylines with Sentence-Level Language Models

google-research/google-research ACL 2020

We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives.

Language Modelling Sentence Embeddings +1

Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

microsoft/recommenders TACL 2019

Our approach decouples learning the transformation from the source language to the target language into (a) learning rotations for language-specific embeddings to align them to a common space, and (b) learning a similarity metric in the common space to model similarities between the embeddings.

Bilingual Lexicon Induction Multilingual Word Embeddings +1

Contextual String Embeddings for Sequence Labeling

zalandoresearch/flair COLING 2018

Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.

Chunking Language Modelling +4

Named Entity Recognition with Bidirectional LSTM-CNNs

flairNLP/flair TACL 2016

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.

Entity Linking Feature Engineering +2

StarSpace: Embed All The Things!

facebookresearch/ParlAI 12 Sep 2017

A framework for training and evaluating AI models on a variety of openly available dialogue datasets.

Text Classification Word Embeddings

Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition

deepmipt/DeepPavlov 27 Sep 2017

Named Entity Recognition (NER) is one of the most common tasks of the natural language processing.

Named Entity Recognition NER +1