Girishkumar Ponkiya


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Looking inside Noun Compounds: Unsupervised Prepositional and Free Paraphrasing
Girishkumar Ponkiya | Rudra Murthy | Pushpak Bhattacharyya | Girish Palshikar
Findings of the Association for Computational Linguistics: EMNLP 2020

A noun compound is a sequence of contiguous nouns that acts as a single noun, although the predicate denoting the semantic relation between its components is dropped. Noun Compound Interpretation is the task of uncovering the relation, in the form of a preposition or a free paraphrase. Prepositional paraphrasing refers to the use of preposition to explain the semantic relation, whereas free paraphrasing refers to invoking an appropriate predicate denoting the semantic relation. In this paper, we propose an unsupervised methodology for these two types of paraphrasing. We use pre-trained contextualized language models to uncover the ‘missing’ words (preposition or predicate). These language models are usually trained to uncover the missing word/words in a given input sentence. Our approach uses templates to prepare the input sequence for the language model. The template uses a special token to indicate the missing predicate. As the model has already been pre-trained to uncover a missing word (or a sequence of words), we exploit it to predict missing words for the input sequence. Our experiments using four datasets show that our unsupervised approach (a) performs comparably to supervised approaches for prepositional paraphrasing, and (b) outperforms supervised approaches for free paraphrasing. Paraphrasing (prepositional or free) using our unsupervised approach is potentially helpful for NLP tasks like machine translation and information extraction.


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Towards a Standardized Dataset for Noun Compound Interpretation
Girishkumar Ponkiya | Kevin Patel | Pushpak Bhattacharyya | Girish K Palshikar
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Treat us like the sequences we are: Prepositional Paraphrasing of Noun Compounds using LSTM
Girishkumar Ponkiya | Kevin Patel | Pushpak Bhattacharyya | Girish Palshikar
Proceedings of the 27th International Conference on Computational Linguistics

Interpreting noun compounds is a challenging task. It involves uncovering the underlying predicate which is dropped in the formation of the compound. In most cases, this predicate is of the form VERB+PREP. It has been observed that uncovering the preposition is a significant step towards uncovering the predicate. In this paper, we attempt to paraphrase noun compounds using prepositions. We consider noun compounds and their corresponding prepositional paraphrases as parallelly aligned sequences of words. This enables us to adapt different architectures from cross-lingual embedding literature. We choose the architecture where we create representations of both noun compound (source sequence) and its corresponding prepositional paraphrase (target sequence), such that their sim- ilarity is high. We use LSTMs to learn these representations. We use these representations to decide the correct preposition. Our experiments show that this approach performs considerably well on different datasets of noun compounds that are manually annotated with prepositions.


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On Why Coarse Class Classification is Bottleneck in Noun Compound Interpretation
Girishkumar Ponkiya | Pushpak Bhattacharyya | Girish K. Palshikar
Proceedings of the 13th International Conference on Natural Language Processing