| Challenge: | Word Embeddings have become a standard for word representations, with vector cosine being the only similarity metric. |
| Approach: | They propose to use rank-based similarity estimation metrics to measure word similarity . they find WE outperforms vector cosine in the recent outlier detection task . |
| Outcome: | The proposed rank-based measure outperforms vector cosine in the recent outlier detection task. |
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Correlation Coefficients and Semantic Textual Similarity (N19-1)
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| Challenge: | Existing research into semantic textual similarity has focused on word embeddings . little attention has been devoted to similarity measures between word embeds - a new study shows . |
| Approach: | They show that cosine similarity is essentially equivalent to the Pearson correlation coefficient for all common word vectors. |
| Outcome: | The proposed model outperforms the existing model on word-level and sentence-level similarity benchmarks. |
On the Correlation of Word Embedding Evaluation Metrics (2020.lrec-1)
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| Challenge: | Word embeddings are geometrical representations of word paradigmatics and syntagmatics. |
| Approach: | They propose to investigate evaluation metrics on various datasets to find correlations . they propose a fast solution to select the best word embeddings among many others . |
| Outcome: | The proposed method could be used to select the best word embeddings among many others. |
Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words (2022.acl-short)
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| Challenge: | We find that word similarities estimated by cosine over contextual embeddings are understated and trace this effect to training data frequency. |
| Approach: | They propose to use cosine similarity to estimate word similarities in contextual embeddings to trace this effect to training data frequency. |
| Outcome: | The proposed model underestimates similarity between frequent and low frequency words even after controlling for polysemy and other factors. |
Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing (N19-1)
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| Challenge: | Existing methods to estimate document similarity based on word embeddings are mediocre . a recent study compared word and sentence embedded documents to a similarity estimate using matrix norms. |
| Approach: | They propose to combine word embeddings with matrix norms to obtain a similarity estimate. |
| Outcome: | The proposed method produces superior results for most of the investigated matrix norms compared to the classical cosine measure and several other similarity estimates. |
Sentiment Classification Using Document Embeddings Trained with Cosine Similarity (P19-2)
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| Challenge: | Existing document embedding models map each document to a dense, low-dimensional vector in continuous vector space. |
| Approach: | They propose to train document embeddings using cosine similarity instead of dot product . they focus on document sentiment classification of long movie reviews . |
| Outcome: | The proposed model improves document embedding accuracy by using cosine similarity instead of dot product on the IMDB dataset while using feature combination with Naive Bayes weighted bag of n-grams achieves 93.68% accuracy. |
Quantifying Context Overlap for Training Word Embeddings (D18-1)
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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. |
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities (L18-1)
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| Challenge: | Standard word embeddings lack the ability to distinguish senses of a word by projecting them to exactly one vector. |
| Approach: | They propose to retrofit standard word embeddings to produce sense-aware embeddable vectors using external resources as sense inventories. |
| Outcome: | The proposed method improves word similarity and relatedness scores on multiple word embeddings and established word similarities, sometimes up to an impressive margin of +0.15 Spearman correlation score. |
Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information (N19-1)
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| Challenge: | Existing evaluation metrics for machine translation are difficult to address word meaning because it is a surface-level metric. |
| Approach: | They propose to use word embeddings, sentence-level tf-idf, and cosine similarity between two word embeds as features, weight, and the distance between two features as features. |
| Outcome: | The proposed metric can evaluate machine translation based on word meaning . it achieves highest correlation with human judgment among several representative metrics. |
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. |
| Outcome: | The proposed approach outperforms most recent methods while being much faster and trivial to implement. |
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings (P19-1)
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| Challenge: | Empirical analysis of word embeddings of ambiguous words is limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. |
| Approach: | They present a large dataset based on manual Wikipedia annotations and word senses, where word sense from different words are related by semantic classes. |
| Outcome: | The proposed method can predict whether a word is single-sense or multi-sensor, if the sense is frequent, and it can predict rare senses. |