Learning Word Embeddings
23 papers with code • 0 benchmarks • 0 datasets
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On Learning Word Embeddings From Linguistically Augmented Text Corpora
Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations.
Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories
Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases.
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
Learning word embeddings has received a significant amount of attention recently.
Cluster Labeling by Word Embeddings and WordNet's Hypernymy
Cluster labeling is the assignment of representative labels to clusters obtained from the organization of a document collection.
Quantifying Context Overlap for Training Word Embeddings
Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations.
Exploration on Grounded Word Embedding: Matching Words and Images with Image-Enhanced Skip-Gram Model
Word embedding is designed to represent the semantic meaning of a word with low dimensional vectors.
Encoding Sentiment Information into Word Vectors for Sentiment Analysis
General-purpose pre-trained word embeddings have become a mainstay of natural language processing, and more recently, methods have been proposed to encode external knowledge into word embeddings to benefit specific downstream tasks.
Model-Free Context-Aware Word Composition
Word composition is a promising technique for representation learning of large linguistic units (e. g., phrases, sentences and documents).
Learning Word Embeddings for Low-Resource Languages by PU Learning
In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved.
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings
In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction.
