Kevin Patel


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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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Indian Language Wordnets and their Linkages with Princeton WordNet
Diptesh Kanojia | Kevin Patel | Pushpak Bhattacharyya
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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Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour
Sandeep Mathias | Diptesh Kanojia | Kevin Patel | Samarth Agrawal | Abhijit Mishra | Pushpak Bhattacharyya
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Predicting a reader’s rating of text quality is a challenging task that involves estimating different subjective aspects of the text, like structure, clarity, etc. Such subjective aspects are better handled using cognitive information. One such source of cognitive information is gaze behaviour. In this paper, we show that gaze behaviour does indeed help in effectively predicting the rating of text quality. To do this, we first we model text quality as a function of three properties - organization, coherence and cohesion. Then, we demonstrate how capturing gaze behaviour helps in predicting each of these properties, and hence the overall quality, by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. We also hypothesize that if a reader has fully understood the text, the corresponding gaze behaviour would give a better indication of the assigned rating, as opposed to partial understanding. Our experiments validate this hypothesis by showing greater agreement between the given rating and the predicted rating when the reader has a full understanding of the text.


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Towards Lower Bounds on Number of Dimensions for Word Embeddings
Kevin Patel | Pushpak Bhattacharyya
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Word embeddings are a relatively new addition to the modern NLP researcher’s toolkit. However, unlike other tools, word embeddings are used in a black box manner. There are very few studies regarding various hyperparameters. One such hyperparameter is the dimension of word embeddings. They are rather decided based on a rule of thumb: in the range 50 to 300. In this paper, we show that the dimension should instead be chosen based on corpus statistics. More specifically, we show that the number of pairwise equidistant words of the corpus vocabulary (as defined by some distance/similarity metric) gives a lower bound on the the number of dimensions , and going below this bound results in degradation of quality of learned word embeddings. Through our evaluations on standard word embedding evaluation tasks, we show that for dimensions higher than or equal to the bound, we get better results as compared to the ones below it.

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Adapting Pre-trained Word Embeddings For Use In Medical Coding
Kevin Patel | Divya Patel | Mansi Golakiya | Pushpak Bhattacharyya | Nilesh Birari
BioNLP 2017

Word embeddings are a crucial component in modern NLP. Pre-trained embeddings released by different groups have been a major reason for their popularity. However, they are trained on generic corpora, which limits their direct use for domain specific tasks. In this paper, we propose a method to add task specific information to pre-trained word embeddings. Such information can improve their utility. We add information from medical coding data, as well as the first level from the hierarchy of ICD-10 medical code set to different pre-trained word embeddings. We adapt CBOW algorithm from the word2vec package for our purpose. We evaluated our approach on five different pre-trained word embeddings. Both the original word embeddings, and their modified versions (the ones with added information) were used for automated review of medical coding. The modified word embeddings give an improvement in f-score by 1% on the 5-fold evaluation on a private medical claims dataset. Our results show that adding extra information is possible and beneficial for the task at hand.


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Are Word Embedding-based Features Useful for Sarcasm Detection?
Aditya Joshi | Vaibhav Tripathi | Kevin Patel | Pushpak Bhattacharyya | Mark Carman
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Detection of Multiword Expressions for Hindi Language using Word Embeddings and WordNet-based Features
Dhirendra Singh | Sudha Bhingardive | Kevin Patel | Pushpak Bhattacharyya
Proceedings of the 12th International Conference on Natural Language Processing