Sudha Bhingardive


2016

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Meaning Matters: Senses of Words are More Informative than Words for Cross-domain Sentiment Analysis
Raksha Sharma | Sudha Bhingardive | Pushpak Bhattacharyya
Proceedings of the 13th International Conference on Natural Language Processing

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Detecting Most Frequent Sense using Word Embeddings and BabelNet
Harpreet Singh Arora | Sudha Bhingardive | Pushpak Bhattacharyya
Proceedings of the 8th Global WordNet Conference (GWC)

Since the inception of the SENSEVAL evaluation exercises there has been a great deal of recent research into Word Sense Disambiguation (WSD). Over the years, various supervised, unsupervised and knowledge based WSD systems have been proposed. Beating the first sense heuristics is a challenging task for these systems. In this paper, we present our work on Most Frequent Sense (MFS) detection using Word Embeddings and BabelNet features. The semantic features from BabelNet viz., synsets, gloss, relations, etc. are used for generating sense embeddings. We compare word embedding of a word with its sense embeddings to obtain the MFS with the highest similarity. The MFS is detected for six languages viz., English, Spanish, Russian, German, French and Italian. However, this approach can be applied to any language provided that word embeddings are available for that language.

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IndoWordNet::Similarity- Computing Semantic Similarity and Relatedness using IndoWordNet
Sudha Bhingardive | Hanumant Redkar | Prateek Sappadla | Dhirendra Singh | Pushpak Bhattacharyya
Proceedings of the 8th Global WordNet Conference (GWC)

Semantic similarity and relatedness measures play an important role in natural language processing applications. In this paper, we present the IndoWordNet::Similarity tool and interface, designed for computing the semantic similarity and relatedness between two words in IndoWordNet. A java based tool and a web interface have been developed to compute this semantic similarity and relatedness. Also, Java API has been developed for this purpose. This tool, web interface and the API are made available for the research purpose.

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Detection of Compound Nouns and Light Verb Constructions using IndoWordNet
Dhirendra Singh | Sudha Bhingardive | Pushpak Bhattacharyyaa
Proceedings of the 8th Global WordNet Conference (GWC)

Detection of MultiWord Expressions (MWEs) is one of the fundamental problems in Natural Language Processing. In this paper, we focus on two categories of MWEs - Compound Nouns and Light Verb Constructions. These two categories can be tackled using knowledge bases, rather than pure statistics. We investigate usability of IndoWordNet for the detection of MWEs. Our IndoWordNet based approach uses semantic and ontological features of words that can be extracted from IndoWordNet. This approach has been tested on Indian languages viz., Assamese, Bengali, Hindi, Konkani, Marathi, Odia and Punjabi. Results show that ontological features are found to be very useful for the detection of light verb constructions, while use of semantic properties for the detection of compound nouns is found to be satisfactory. This approach can be easily adapted by other Indian languages. Detected MWEs can be interpolated into WordNets as they help in representing semantic knowledge.

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Multiword Expressions Dataset for Indian Languages
Dhirendra Singh | Sudha Bhingardive | Pushpak Bhattacharyya
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Multiword Expressions (MWEs) are used frequently in natural languages, but understanding the diversity in MWEs is one of the open problem in the area of Natural Language Processing. In the context of Indian languages, MWEs play an important role. In this paper, we present MWEs annotation dataset created for Indian languages viz., Hindi and Marathi. We extract possible MWE candidates using two repositories: 1) the POS-tagged corpus and 2) the IndoWordNet synsets. Annotation is done for two types of MWEs: compound nouns and light verb constructions. In the process of annotation, human annotators tag valid MWEs from these candidates based on the standard guidelines provided to them. We obtained 3178 compound nouns and 2556 light verb constructions in Hindi and 1003 compound nouns and 2416 light verb constructions in Marathi using two repositories mentioned before. This created resource is made available publicly and can be used as a gold standard for Hindi and Marathi MWE systems.

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Synset Ranking of Hindi WordNet
Sudha Bhingardive | Rajita Shukla | Jaya Saraswati | Laxmi Kashyap | Dhirendra Singh | Pushpak Bhattacharyya
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Word Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining the sense of a word in a particular context. It has been observed that such approaches often find it difficult to beat the WordNet First Sense (WFS) baseline which assigns the sense irrespective of context. In this paper, we present our work on creating the WFS baseline for Hindi language by manually ranking the synsets of Hindi WordNet. A ranking tool is developed where human experts can see the frequency of the word senses in the sense-tagged corpora and have been asked to rank the senses of a word by using this information and also his/her intuition. The accuracy of WFS baseline is tested on several standard datasets. F-score is found to be 60%, 65% and 55% on Health, Tourism and News datasets respectively. The created rankings can also be used in other NLP applications viz., Machine Translation, Information Retrieval, Text Summarization, etc.

2015

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Using Word Embeddings for Bilingual Unsupervised WSD
Sudha Bhingardive | Dhirendra Singh | Rudramurthy V | Pushpak Bhattacharyya
Proceedings of the 12th International Conference on 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

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Unsupervised Most Frequent Sense Detection using Word Embeddings
Sudha Bhingardive | Dhirendra Singh | Rudramurthy V | Hanumant Redkar | Pushpak Bhattacharyya
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Semi-Automatic Extension of Sanskrit Wordnet using Bilingual Dictionary
Sudha Bhingardive | Tanuja Ajotikar | Irawati Kulkarni | Malhar Kulkarni | Pushpak Bhattacharyya
Proceedings of the Seventh Global Wordnet Conference

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IndoWordnet Visualizer: A Graphical User Interface for Browsing and Exploring Wordnets of Indian Languages
Devendra Singh Chaplot | Sudha Bhingardive | Pushpak Bhattacharyya
Proceedings of the Seventh Global Wordnet Conference

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Merging Verb Senses of Hindi WordNet using Word Embeddings
Sudha Bhingardive | Ratish Puduppully | Dhirendra Singh | Pushpak Bhattacharyya
Proceedings of the 11th International Conference on Natural Language Processing

2013

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Neighbors Help: Bilingual Unsupervised WSD Using Context
Sudha Bhingardive | Samiulla Shaikh | Pushpak Bhattacharyya
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)