Papers by Girishkumar Ponkiya
FrameNet-assisted Noun Compound Interpretation (2021.findings-acl)
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| Challenge: | Existing methods for predicting semantic labels for noun compound interpretation are difficult. |
| Approach: | They propose to predict semantic labels in a continuous embedding space using FrameNet data. |
| Outcome: | The proposed method performs well on unseen labels, with 5% and 2% improvement over baselines for frame and FE prediction. |
Treat us like the sequences we are: Prepositional Paraphrasing of Noun Compounds using LSTM (C18-1)
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| Challenge: | Using prepositions, noun compounds are interpreted in two ways: labelling and paraphrasing. |
| Approach: | They propose to paraphrase noun compounds using prepositions by using parallelly aligned sequences of words. |
| Outcome: | The proposed approach performs well on datasets manually annotated with prepositions. |
Looking inside Noun Compounds: Unsupervised Prepositional and Free Paraphrasing (2020.findings-emnlp)
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| Challenge: | Noun compound interpretation is the task of uncovering the semantic relation between the components of a noun compound. |
| Approach: | They propose an unsupervised method for identifying such relations between the components of a noun compound using pre-trained contextualized language models. |
| Outcome: | The proposed method outperforms supervised approaches for free paraphrasing and prepositional paraphrases using pre-trained language models to uncover ‘missing’ words. |
Towards a Standardized Dataset for Noun Compound Interpretation (L18-1)
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| Challenge: | Noun compounds are interesting constructs in Natural Language Processing . lack of standardized set of relation inventories and annotated datasets hinders interpretation . |
| Approach: | They propose a dataset that uses FrameNet as its semantic relation inventory to examine noun compounds. |
| Outcome: | The proposed dataset is linguistically grounded and uses FrameNet as its semantic relation inventory. |