DOCTORAL ABSTRACT

Named Entity Extraction for Knowledgebase
Enhancement
Priya Radhakrishnan
IIIT Hyderabad, India
priya.r@research.iiit.ac.in

Abstract
Past decade witnessed an explosive growth in the amount of unstructured data, especiallyin the public domain, mainly due to Web 2.0 and social media. This led to the creation ofapplications, called information extractors, that extract structured information from unstructured data. The extracted information is stored in a Knowledge Base (KB). KB stores facts
about entities like name, type and other attributes.
My PhD thesis entitled ‘Named Entity Extraction for Knowledgebase Enhancement’ deals
with information extraction on named entities with the purpose of enhancing a KB. The
enhanced KB is in turn used by the information extraction task to refine the extraction
process. Thus, KB provides structure and guidance to the extraction task, and gets enhancedby the results of the extraction task. Here we see that the tasks of entity extraction andKB enhancement are mutually dependent and mutually beneficial. Hence in my research I
propose methods to enhance both the tasks, in an effort to build a strong and sound namedentity extraction system.
Named Entity Extraction, also known as Entity Linking (EL) in scientific literature, is thetask of determining the identity of entities mentioned in text. EL helps automatic extractionof structured information about entities from unstructured data, which is stored in the KB.
EL consists of Mention Detection and Entity Disambiguation. In my research, I propose
methods to enhance mention detection, entity disambiguation and KB enhancement.
Textual content to be processed by information extractor, typically is about multiple
named entities. The information extractor has to identify the named entities that are important to the content, a.k.a. salient named entities. My thesis proposes a method to identify
salient named entity of text [6]. Salience of a named entity can also be judged by learning
how the named entity is semantically related to other named entities mentioned in the document. We propose a method to identify the semantic relations within named entities [4].
The proposed methods help to improve mention detection.
Performance of Named Entity Extraction methods depend on the size and structure of
the context of the named entity mention in the text. While bigger size and better structureof context result in improved performance of the Named Entity Extraction, smaller size andpoor structure of context result in reduced performance. We propose three different Named
Entity Extraction approaches tailored to the varying size and structure of the context in thisthesis [7, 1, 3]. The proposed methods perform on par with state-of-the-art methods withimproved latency.

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Named Entity Extraction approaches that work on lesser context and lower structure,
increasingly depend on non-textual signal like global coherence of entities in the KB. Degreeof connectivity of an entity in the KB directly affects an EL systems ability to correctly
link mentions in text to the entity in KB. This causes many EL systems to perform wellfor entities well connected to other entities in KB, bringing focus to connection density ofKB in EL. We propose ELDEN, an EL system that densifies the KB with co-occurrence
statistics from a large text corpus and uses the densified KB to train entity embeddings.
Entity similarity measured using these trained entity embeddings results in improved EL [5].
ELDEN outperforms state-of-the-art EL system on benchmark datasets.
Proof of the pudding is in the eating. We demonstrate use of entities stored in KB
in an application, an information retrieval task that is improved by use of KB entities. We
propose a novel method for enhancing classification performance of research papers into ACM
computer science categories using KB entities, namely Wikipedia and Freebase entities [2].
All through my research I have proposed methods of improving Named Entity Extraction using KBs. The approaches model word and entity representations for automatic text
understanding using KB. My current and future explorations will also be towards building
better representations of entity and document towards achieving this goal.

References[1] R. Bansal, S. Panem, P. Radhakrishnan, M. Gupta, and V. Varma. Linking entities
in #microposts. In M. Rowe, M. Stankovic, and A. Dadzie, editors, Proceedings of thethe 4th Workshop on Making Sense of Microposts co-located with the 23rd International
World Wide Web Conference (WWW 2014), Seoul, Korea, April 7th, 2014., pages 71–72.
CEUR-WS.org, 2014.
[2] S. Gupta, P. Radhakrishnan, M. Gupta, V. Varma, and U. Gupta. Enhancing categorization of computer science research papers using knowledge bases. In Proceedings ofthe First Workshop on Knowledge Graphs and Semantics for Text Retrieval and Analysis (KG4IR 2017) co-located with the 40th International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR 2017), Shinjuku, Tokyo,Japan, August 11, 2017., pages 38–42, 2017.
[3] P. Radhakrishnan, R. Bansal, M. Gupta, and V. Varma. Exploiting Wikipedia Inlinksfor Linking Entities in Queries. In Proceedings of the First International Workshop on
Entity Recognition & Disambiguation, ERD ’14, pages 101–104, New York, NY, USA,2014. ACM.
[4] P. Radhakrishnan, M. Gupta, and V. Varma. Modeling the evolution of product entities.
In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’14, pages 923–926, New York, NY, USA, 2014.
ACM.
[5] P. Radhakrishnan, M. Gupta, and V. Varma. Elden: Improved entity linking using
densified knowledge graphs. In 16th Annual Conference of the North American Chapter ofthe Association for Computational Linguistics: Human Language Technologies, NAACLHLT ’18, 2018.
[6] P. Radhakrishnan, G. Jawahar, M. Gupta, and V. Varma. Sneit: Salient named entity
identification in tweets. Computación y Sistemas, 21(4):665679, 2017.
[7] V. Varma, B. Ghosh, M. Soundararajan, D. Aggarwal, and P. Radhakrishnan. IIIT
hyderabad at TAC 2012. In Proceedings of the Fifth Text Analysis Conference, TAC
2012, Gaithersburg, Maryland, USA, November 5-6, 2012. NIST, 2012.

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