Saeedeh Shekarpour


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CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
Manoj Prabhakar Kannan Ravi | Kuldeep Singh | Isaiah Onando Mulang’ | Saeedeh Shekarpour | Johannes Hoffart | Jens Lehmann
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to accomplish the EL task. The first transformer model identifies surface forms (entity mentions) in a given text. For each mention, a second transformer model is employed to classify the target entity among a predefined candidates list. The latter transformer is fed by an enriched context captured from the sentence (i.e. local context), and entity description gained from Wikipedia. Such external contexts have not been used in state of the art EL approaches. Our empirical study was conducted on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical results suggest that CHOLAN outperforms state-of-the-art approaches on standard datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.

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KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
Abhishek Nadgeri | Anson Bastos | Kuldeep Singh | Isaiah Onando Mulang’ | Johannes Hoffart | Saeedeh Shekarpour | Vijay Saraswat
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph
Saeedeh Shekarpour | Abhishek Nadgeri | Kuldeep Singh
Proceedings of the First Workshop on Interactive and Executable Semantic Parsing

In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility. However, their applicability, particularly in specific domains such as the biomedical domain, has not gained wide acceptance due to their “black box” nature, which hinders transparency, fairness, and accountability of QA systems. Therefore, users are unable to understand how and why particular questions have been answered, whereas some others fail. To address this challenge, in this paper, we develop an automatic approach for generating explanations during various stages of a pipeline-based QA system. Our approach is a supervised and automatic approach which considers three classes (i.e., success, no answer, and wrong answer) for annotating the output of involved QA components. Upon our prediction, a template explanation is chosen and integrated into the output of the corresponding component. To measure the effectiveness of the approach, we conducted a user survey as to how non-expert users perceive our generated explanations. The results of our study show a significant increase in the four dimensions of the human factor from the Human-computer interaction community.


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Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text
Ahmad Sakor | Isaiah Onando Mulang’ | Kuldeep Singh | Saeedeh Shekarpour | Maria Esther Vidal | Jens Lehmann | Sören Auer
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a light-weight linguistic approach relying on a background knowledge graph. Falcon performs joint entity and relation linking of a short text by leveraging several fundamental principles of English morphology (e.g. compounding, headword identification) and utilizes an extended knowledge graph created by merging entities and relations from various knowledge sources. It uses the context of entities for finding relations and does not require training data. Our empirical study using several standard benchmarks and datasets show that Falcon significantly outperforms state-of-the-art entity and relation linking for short text query inventories.