| Challenge: | Existing methods to estimate word similarities are to embed words in vector space and then calculate similarities between corresponding vectors. |
| Approach: | They propose a method that explicitly counts second-order co-occurrences to estimate word similarities from streams. |
| Outcome: | The proposed method is scalable, converges rapidly, behaves robustly under parameter changes, and captures word similarities on par with state-of-the-art word embeddings. |
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| Challenge: | Experimental results show that word embeddings can be improved using word embeds . word embedings are a popular form of natural language processing . |
| Approach: | They propose to estimate second order co-occurrence relations based on context overlap . they use the augmented data to enhance word embeddings learning . |
| Outcome: | The proposed model improves word vectors for word similarity and downstream NLP tasks. |
Correlations between Word Vector Sets (D19-1)
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| Challenge: | Similarity measures based on word embeddings are easily competing with deep learning and expert-engineered systems on unsupervised semantic textual similarity tasks. |
| Approach: | They propose a new approach to measure word embeddings using pooling operations and correlation coefficients instead of pooling . they also propose centered kernel alignment as a natural generalisation of squared cosine similarity for sets of word vectors. |
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Estimating Mutual Information Between Dense Word Embeddings (2020.acl-main)
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| Challenge: | Word embedding-based similarity measures are among the top-performing methods on unsupervised semantic textual similarity tasks. |
| Approach: | They propose a word embedding similarity measure that is a viable alternative to correlations and gives an excellent signal that correlates well with human judgements of similarity. |
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CoSimLex: A Resource for Evaluating Graded Word Similarity in Context (2020.lrec-1)
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Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, Mark Granroth-Wilding
| Challenge: | Existing methods to evaluate word embeddings ignore context and treat words in isolation. |
| Approach: | They propose to build a new word embeddings-based dataset that provides context-dependent similarity measures. |
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McPhraSy: Multi-Context Phrase Similarity and Clustering (2022.findings-emnlp)
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| Challenge: | Existing methods for estimating phrase similarity use the phrase context only during training, instead relying on the phrase itself. |
| Approach: | They propose a novel algorithm that leverages multiple contexts during inference to estimate the similarity of phrases based on multiple context. |
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Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora (2020.acl-main)
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| Challenge: | comparing two corpus texts and searching for words that differ in their usage between them is a common problem in digital humanities and computational social science. |
| Approach: | They propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word. |
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Solving Cosine Similarity Underestimation between High Frequency Words by ℓ2 Norm Discounting (2023.findings-acl)
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| Challenge: | Existing methods to estimate cosine similarity between words have been proposed, but no solution has been proposed. |
| Approach: | They propose a method to discount the l2 norm of contextualised word embedding by the frequency of that word in a corpus when measuring the cosine similarities between words. |
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Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences (N19-1)
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| Challenge: | Existing word embedding algorithms make a strong assumption that words are semantically related only if they co-occur locally within a window of fixed size. |
| Approach: | They propose a graph-based word embedding method that relies on locality to capture the semantic association between words that co-occur frequently but non-locally within documents. |
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Unsupervised Learning of Distributional Relation Vectors (P18-1)
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| Challenge: | Existing word embedding models rely on co-occurrence statistics to learn vector representations of word meaning. |
| Approach: | They propose a method which directly learns relation vectors from co-occurrence statistics. |
| Outcome: | The proposed method is based on a variant of GloVe, which has an explicit connection between word vectors and PMI weighted co-occurrence vectors. |
Just Rank: Rethinking Evaluation with Word and Sentence Similarities (2022.acl-long)
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| Challenge: | Word and sentence similarity tasks are the de facto evaluation method for embeddings. |
| Approach: | They propose a new intrinsic evaluation method called EvalRank which shows a much stronger correlation with downstream tasks. |
| Outcome: | The proposed method shows a much stronger correlation with downstream tasks and is released for future benchmarking purposes. |