Word Similarity
111 papers with code • 0 benchmarks • 2 datasets
Calculate a numerical score for the semantic similarity between two words.
Benchmarks
These leaderboards are used to track progress in Word Similarity
Libraries
Use these libraries to find Word Similarity models and implementations
AnlamVer
Bangla Word Analogy
Most implemented papers
Efficient Estimation of Word Representations in Vector Space
mindspore-courses/DeepNLP-models-MindSpore
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We propose two novel model architectures for computing continuous vector representations of words from very large data sets.
Enriching Word Vectors with Subword Information
A vector representation is associated to each character $n$-gram; words being represented as the sum of these representations.
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
The postprocessing is empirically validated on a variety of lexical-level intrinsic tasks (word similarity, concept categorization, word analogy) and sentence-level tasks (semantic textural similarity and { text classification}) on multiple datasets and with a variety of representation methods and hyperparameter choices in multiple languages; in each case, the processed representations are consistently better than the original ones.
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
Maybe the single most important goal of representation learning is making subsequent learning faster.
Calculating the similarity between words and sentences using a lexical database and corpus statistics
To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database.
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech
my-yy/s2v_rc
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In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar.
Unsupervised Multilingual Word Embeddings
Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space.
SemGloVe: Semantic Co-occurrences for GloVe from BERT
In this paper, we propose SemGloVe, which distills semantic co-occurrences from BERT into static GloVe word embeddings.
WordRank: Learning Word Embeddings via Robust Ranking
Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses.
Definition Modeling: Learning to define word embeddings in natural language
Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks.
