Papers with dependency-based embeddings
attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines (N18-1)
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| Challenge: | popular word embeddings are used to learn vector representations from the context of words. |
| Approach: | They propose a framework for jointly learning embeddings for words and contextual attributes based on factorization machines. |
| Outcome: | The proposed framework improves on a text classification task compared to learning embeddings independently. |
Robust Cross-Lingual Hypernymy Detection Using Dependency Context (N18-1)
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| Challenge: | Existing approaches to cross-lingual hypernymy detection are sparse and can be trained on related languages with negligible loss of performance. |
| Approach: | They propose a family of unsupervised approaches for cross-lingual hypernymy detection which learns sparse, bilingual word embeddings based on dependency contexts. |
| Outcome: | The proposed approach significantly improves performance on this task, compared to approaches based only on lexical context. |
Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis (2020.acl-main)
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| Challenge: | Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information. |
| Approach: | They propose an end-to-end aspect-based sentiment analysis solution that integrates syntactical information with part-of-speech embeddings and dependency-based embeddables to enhance the performance of the aspect extractor. |
| Outcome: | The proposed solution outperforms the state-of-the-art models on SemEval-2014 dataset in both subtasks. |