Browsing by Author "Mankame, P."
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Item Information extraction for conversational systems in Indian languages - ARnekt IECSIL(2018) Hb, B.G.; Kp, S.; Reshma, U.; Kale, M.; Mankame, P.; Kulkarni, G.; Kale, A.; Anand, Kumar, M.Data being the new source of wealth, mining intelligence from every possible units of it, has become today�s salient feature in many fields. Text data is not limited to one language and this has showcased its usability in creating multiple applications from various languages. Development of Indian languages is just getting better both in terms of resource and application specific. Information Extraction for Conversational Systems in Indian Languages - Arnekt IECSIL has taken its step in creating its own resource in Indian languages (Hindi, Kannada, Malayalam, Tamil and Telugu) for Named Entity Recognition (NER) and Information Extraction (IE) tasks. This overview paper will be detailing more on the existing Indian language corpora development and the steps taken for building our own corpus along with its statistics. � 2016 Association for Computing Machinery.Item Information extraction for conversational systems in Indian languages - ARnekt IECSIL(Association for Computing Machinery acmhelp@acm.org, 2018) Hb, B.G.; Kp, S.; Reshma, U.; Kale, M.; Mankame, P.; Kulkarni, G.; Kale, A.; Anand Kumar, M.Data being the new source of wealth, mining intelligence from every possible units of it, has become today’s salient feature in many fields. Text data is not limited to one language and this has showcased its usability in creating multiple applications from various languages. Development of Indian languages is just getting better both in terms of resource and application specific. Information Extraction for Conversational Systems in Indian Languages - Arnekt IECSIL has taken its step in creating its own resource in Indian languages (Hindi, Kannada, Malayalam, Tamil and Telugu) for Named Entity Recognition (NER) and Information Extraction (IE) tasks. This overview paper will be detailing more on the existing Indian language corpora development and the steps taken for building our own corpus along with its statistics. © 2016 Association for Computing Machinery.Item Overview of Arnekt IECSIL at Fire-2018 track on information extraction for conversational systems in Indian languages(2018) Barathi, Ganesh, H.B.; Soman, K.P.; Reshma, U.; Kale, M.; Mankame, P.; Kulkarni, G.; Kale, A.; Anand, Kumar, M.This overview paper describes the first shared task on Information Extractor for Conversational Systems in Indian Languages (IECSIL) which has been organized by FIRE 2018. Motivated by the need of Information Extractor, corpora has been developed to perform the Named Entity Recognition (Task A) and Relation Extraction (Task B) for five Indian languages (Hindi, Tamil, Malayalam, Telugu and Kannada). Task A is to identify and classify the named entities to one of the many classes and Task B is to extract the relation among the entities present in the sentences. Altogether, nearly 100 submission of 10 different teams were evaluated. In this paper, we have given an overview of the approaches and also discussed the results that the participated teams have attained. � 2018 CEUR-WS. All Rights Reserved.Item Overview of Arnekt IECSIL at Fire-2018 track on information extraction for conversational systems in Indian languages(CEUR-WS ceurws@sunsite.informatik.rwth-aachen.de, 2018) Barathi Ganesh, H.; Padannayil, K.P.; Reshma, U.; Kale, M.; Mankame, P.; Kulkarni, G.; Kale, A.; Anand Kumar, M.This overview paper describes the first shared task on Information Extractor for Conversational Systems in Indian Languages (IECSIL) which has been organized by FIRE 2018. Motivated by the need of Information Extractor, corpora has been developed to perform the Named Entity Recognition (Task A) and Relation Extraction (Task B) for five Indian languages (Hindi, Tamil, Malayalam, Telugu and Kannada). Task A is to identify and classify the named entities to one of the many classes and Task B is to extract the relation among the entities present in the sentences. Altogether, nearly 100 submission of 10 different teams were evaluated. In this paper, we have given an overview of the approaches and also discussed the results that the participated teams have attained. © 2018 CEUR-WS. All Rights Reserved.