Papers with word vector representations
Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
| Approach: | They propose to combine known tricks and a set of publicly available pre-trained word vector representations to train high-quality representations. |
| Outcome: | The proposed models outperform the current state of the art on a number of tasks while maintaining a high training speed to scale to massive amount of data. |
Subword-level Word Vector Representations for Korean (P18-1)
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| Challenge: | Existing research on word vectors for English focuses on decomposing words into subword units and using subwords to improve performance. |
| Approach: | They propose to decompose Korean words into the jamo-level, beyond the character-level . they develop Korean test sets for word similarity and analogy and make them publicly available . |
| Outcome: | The proposed method outperforms word2vec and character-level skip-grams on similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis. |
Sentence Analogies: Linguistic Regularities in Sentence Embeddings (2020.coling-main)
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| Challenge: | Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships considered in word analogies. |
| Approach: | They propose a number of schemes to induce evaluation data based on lexical analogy data as well as semantic relationships between sentences. |
| Outcome: | The proposed models reflect regularities in lexical analogies and semantic relationships between sentences. |
Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)
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| Challenge: | Existing sentiment lexicons reflect abstract notion of polarity and do not do justice to substantial differences of word polarities between domains. |
| Approach: | They propose to use domain-specific sentiment lexicons to induce initial word intensity scores and train new deep models based on word vector representations to overcome the scarcity of the seed data. |
| Outcome: | The proposed models show that they perform well on review classification and cross-lingual word sentiment prediction. |