Md. Faisal Mahbub Chowdhury


2020

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Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer
Chao Shang | Sarthak Dash | Md. Faisal Mahbub Chowdhury | Nandana Mihindukulasooriya | Alfio Gliozzo
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Extracting lexico-semantic relations as graph-structured taxonomies, also known as taxonomy construction, has been beneficial in a variety of NLP applications. Recently Graph Neural Network (GNN) has shown to be powerful in successfully tackling many tasks. However, there has been no attempt to exploit GNN to create taxonomies. In this paper, we propose Graph2Taxo, a GNN-based cross-domain transfer framework for the taxonomy construction task. Our main contribution is to learn the latent features of taxonomy construction from existing domains to guide the structure learning of an unseen domain. We also propose a novel method of directed acyclic graph (DAG) generation for taxonomy construction. Specifically, our proposed Graph2Taxo uses a noisy graph constructed from automatically extracted noisy hyponym hypernym candidate pairs, and a set of taxonomies for some known domains for training. The learned model is then used to generate taxonomy for a new unknown domain given a set of terms for that domain. Experiments on benchmark datasets from science and environment domains show that our approach attains significant improvements correspondingly over the state of the art.

2019

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Automatic Taxonomy Induction and Expansion
Nicolas Rodolfo Fauceglia | Alfio Gliozzo | Sarthak Dash | Md. Faisal Mahbub Chowdhury | Nandana Mihindukulasooriya
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system. One of its main capabilities is to automatically induce taxonomies from input documents using a hybrid approach that takes advantage of linguistic patterns, semantic web and neural networks. KGIS allows the user to semi-automatically curate and expand the induced taxonomy through a component called Smart SpreadSheet by exploiting distributional semantics. In this paper, we describe these taxonomy induction and expansion features of KGIS. A screencast video demonstrating the system is available in https://ibm.box.com/v/emnlp-2019-demo .

2013

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Exploiting the Scope of Negations and Heterogeneous Features for Relation Extraction: A Case Study for Drug-Drug Interaction Extraction
Md. Faisal Mahbub Chowdhury | Alberto Lavelli
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information
Md. Faisal Mahbub Chowdhury | Alberto Lavelli
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

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FBK: Sentiment Analysis in Twitter with Tweetsted
Md. Faisal Mahbub Chowdhury | Marco Guerini | Sara Tonelli | Alberto Lavelli
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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FBK: Exploiting Phrasal and Contextual Clues for Negation Scope Detection
Md. Faisal Mahbub Chowdhury
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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An Evaluation of the Effect of Automatic Preprocessing on Syntactic Parsing for Biomedical Relation Extraction
Md. Faisal Mahbub Chowdhury | Alberto Lavelli
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Relation extraction (RE) is an important text mining task which is the basis for further complex and advanced tasks. In state-of-the-art RE approaches, syntactic information obtained through parsing plays a crucial role. In the context of biomedical RE previous studies report usage of various automatic preprocessing techniques applied before parsing the input text. However, these studies do not specify to what extent such techniques improve RE results and to what extent they are corpus specific as well as parser specific. In this paper, we aim at addressing these issues by using various preprocessing techniques, two syntactic tree kernel based RE approaches and two different parsers on 5 widely used benchmark biomedical corpora of the protein-protein interaction (PPI) extraction task. We also provide analyses of various corpus characteristics to verify whether there are correlations between these characteristics and the RE results obtained. These analyses of corpus characteristics can be exploited to compare the 5 PPI corpora.

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Combining Tree Structures, Flat Features and Patterns for Biomedical Relation Extraction
Md. Faisal Mahbub Chowdhury | Alberto Lavelli
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Impact of Less Skewed Distributions on Efficiency and Effectiveness of Biomedical Relation Extraction
Md. Faisal Mahbub Chowdhury | Alberto Lavelli
Proceedings of COLING 2012: Posters

2011

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Assessing the practical usability of an automatically annotated corpus
Md. Faisal Mahbub Chowdhury | Alberto Lavelli
Proceedings of the 5th Linguistic Annotation Workshop

2010

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Disease Mention Recognition with Specific Features
Md. Faisal Mahbub Chowdhury | Alberto Lavelli
Proceedings of the 2010 Workshop on Biomedical Natural Language Processing

2009

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Using Treebanking Discriminants as Parse Disambiguation Features
Md. Faisal Mahbub Chowdhury | Yi Zhang | Valia Kordoni
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)