Chinnappa Guggilla


2019

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AI_Blues at FinSBD Shared Task: CRF-based Sentence Boundary Detection in PDF Noisy Text in the Financial Domain
Ditty Mathew | Chinnappa Guggilla
Proceedings of the First Workshop on Financial Technology and Natural Language Processing

2016

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Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks
Chinnappa Guggilla
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29% weighted-F1 accuracy in this sub-task using CNN approach using default network parameters.

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CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks
Chinnappa Guggilla
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared task of corpus-based identification of semantic relations. Proposed approach using CNN-based deep neural networks leveraging pre-compiled word2vec distributional neural embeddings achieved 43.15% weighted-F1 accuracy on subtask-1 (checking existence of a relation between two terms) and 25.24% weighted-F1 accuracy on subtask-2 (classifying relation types).

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CNN- and LSTM-based Claim Classification in Online User Comments
Chinnappa Guggilla | Tristan Miller | Iryna Gurevych
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes propositions according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.