Browsing by Author "Kale, M."

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    DYNA-RANK: Efficient calculation and updation of pagerank
    (2008) Kale, M.; Santhi Thilagam, P.
    The decision of the ranking of web page is very important in web, as its growing and changing very rapidly. Ranking of the results in a search engine for a query plays crucial role for huge database like Web, where one query can have millions of results. The browsing nature of web will mostly depend on the ranking of the search results. The existing approaches for calculating pagerank values are mostly centralized and the ones which are distributed, are not being used for practical purposes because of the scalability reasons. The centralized approaches considers total web as one graph and they calculate the pagerank values of total graph after certain time period, which takes long execution time and can be in days. In the same way updating the graph also compels to recalculate all the pagerank values of all the pages in the graph. This suggests possible applicability of the distributed algorithm to pagerank computations as a replacement for the centralized pagerank calculation algorithm. Considering the importance of the "Ranking" in searching context, our approach DYNA-RANK, focuses upon efficiently calculating and updating Google's pagerank vector using "peer to peer" system. The changes in the web structure will be handled incrementally amongst the peers. DYNA-RANK produces the relative pagerank on each peer. DYNA-RANK is proven to take less computation time and less number of iterations compared to centralized approach. � 2008 IEEE.
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    DYNA-RANK: Efficient calculation and updation of pagerank
    (2008) Kale, M.; Santhi Thilagam, P.S.
    The decision of the ranking of web page is very important in web, as its growing and changing very rapidly. Ranking of the results in a search engine for a query plays crucial role for huge database like Web, where one query can have millions of results. The browsing nature of web will mostly depend on the ranking of the search results. The existing approaches for calculating pagerank values are mostly centralized and the ones which are distributed, are not being used for practical purposes because of the scalability reasons. The centralized approaches considers total web as one graph and they calculate the pagerank values of total graph after certain time period, which takes long execution time and can be in days. In the same way updating the graph also compels to recalculate all the pagerank values of all the pages in the graph. This suggests possible applicability of the distributed algorithm to pagerank computations as a replacement for the centralized pagerank calculation algorithm. Considering the importance of the "Ranking" in searching context, our approach DYNA-RANK, focuses upon efficiently calculating and updating Google's pagerank vector using "peer to peer" system. The changes in the web structure will be handled incrementally amongst the peers. DYNA-RANK produces the relative pagerank on each peer. DYNA-RANK is proven to take less computation time and less number of iterations compared to centralized approach. © 2008 IEEE.
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    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.
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    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.
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    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.
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    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.
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    The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
    (Association for Computational Linguistics (ACL), 2021) Gehrmann, S.; Adewumi, T.; Aggarwal, K.; Ammanamanchi, P.S.; Anuoluwapo, A.; Bosselut, A.; Chandu, K.R.; Clinciu, M.; Das, D.; Dhole, K.D.; Du, W.; Durmus, E.; Dušek, O.; Emezue, C.; Gangal, V.; Gârbacea, C.; Hashimoto, T.; Hou, Y.; Jernite, Y.; Jhamtani, H.; Ji, Y.; Jolly, S.; Kale, M.; Kumar, D.; Ladhak, F.; Madaan, A.; Maddela, M.; Mahajan, K.; Mahamood, S.; Majumder, B.P.; Martins, P.H.; McMillan-Major, A.; Mille, S.; van Miltenburg, E.; Nadeem, M.; Narayan, S.; Nikolaev, V.; Niyongabo, R.A.; Osei, S.; Parikh, A.; Perez-Beltrachini, L.; Rao, N.R.; Raunak, V.; Rodriguez, J.D.; Santhanam, S.; Sedoc, J.; Sellam, T.; Shaikh, S.; Shimorina, A.; Sobrevilla Cabezudo, M.A.S.; Strobelt, H.; Subramani, N.; Xu, W.; Yang, D.; Yerukola, A.; Zhou, J.
    We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate. © 2021 Association for Computational Linguistics