IIIT-H: A Corpus-Driven Co-occurrence Based Probabilistic Model for Noun Compound Paraphrasing
Nitesh Surtani, Arpita Batra, Urmi Ghosh, Soma Paul · 2013
This paper presents a system for automatically generating a set of plausible paraphrases for a given noun compound and rank them in decreasing order of their usage represented by the confidence value provided by the human annotators. Our system implements a corpusdriven probabilistic co-occurrence based model for predicting the paraphrases, that uses a seed list of paraphrases extracted from corpus to predict other paraphrases based on their co-occurrences. The corpus study reveals that the prepositional paraphrases for the noun compounds are quite frequent and well covered but the verb paraphrases, on the other hand, are scarce, revealing the unsuitability of the model for standalone corpus-driven approach. Therefore, to predict other paraphrases, we adopt a two-fold approach: (i) Prediction based on Verb-Verb cooccurrences, in case the seed paraphrases are greater than threshold; and (ii) Prediction based on Semantic Relation of NC, otherwise. The system achieves a comparabale score of 0.23 for the isomorphic system while maintaining a score of 0.26 for the non-isomorphic system.