Papers with learnt model
Functional Distributional Semantics at Scale (2023.starsem-1)
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| Challenge: | Functional Distributional Semantics is a linguistically motivated framework for modelling lexical and sentence-level semantics with truth-conditional functions using distributional information. |
| Approach: | They propose a more expressive lexical model that works over a continuous semantic space. |
| Outcome: | The proposed model improves performance and flexibility and is compatible with present-day machine learning frameworks. |
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios (2022.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) tasks require a large amount of training data and domains are often scarcely labeled. |
| Approach: | They propose a hardness-guided domain adaptation framework for bioNER tasks that leverages domain hardness information to improve the adaptability of the learnt model in low-resource scenarios. |
| Outcome: | The proposed model outperforms the state-of-the-art MetaNER model on biomedical datasets. |
Rule Augmented Unsupervised Constituency Parsing (2021.findings-acl)
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| Challenge: | Recent studies have shown that unsupervised parsing methods do not learn meaningful semantics (not even simple grammar) |
| Approach: | They propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic grammar rules and is independent of the base system. |
| Outcome: | The proposed model is independent of the base system and takes advantage of syntactic grammar rules. |