Automatic creation of domain templates

Filatova, Elena; Hatzivassiloglou, Vasileios; McKeown, Kathleen

doi:10.3115/1273073.1273100

Cited by 28 publications

(29 citation statements)

References 23 publications

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“…The idea of automatic extracting of domain templates from large corpus has been extensively studied, for example, by (Filatova et al, 2006;Chambers and Jurafsky, 2011;Fader et al, 2011 search field becomes closer to ontology learning and knowledge-base population, though the latter task might be more difficult since it requires crossdocument inference (Ji and Grishman, 2011). The idea of simultaneous (coupled, joint) learning of both instances and relation have been justified.…”

Section: Related Workmentioning

confidence: 99%

Towards Never Ending Language Learning for Morphologically Rich Languages

Buraya

Pivovarova²,

Budkov

et al. 2017

Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

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This work deals with ontology learning from unstructured Russian text. We implement one of the components of Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologically rich freeword-order language. We perform several experiments comparing different settings of the training process. We demonstrate that morphological features significantly improve the system precision while seed patterns help to improve the coverage.

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Section: Related Workmentioning

confidence: 99%

Towards Never Ending Language Learning for Morphologically Rich Languages

Buraya

Pivovarova²,

Budkov

et al. 2017

Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

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show abstract

“…The idea of automatic extracting of domain templates from large corpus has been extensively studied, for example, by (Filatova et al, 2006;Chambers and Jurafsky, 2011;Fader et al, 2011). Thus, pattern-based information extraction as re- search field becomes closer to ontology learning and knowledge-base population, though the latter task might be more difficult since it requires crossdocument inference (Ji and Grishman, 2011).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

2017

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The Workshops have been convening for over a decade, with a clear vision and purpose. On one hand, the languages from the Balto-Slavic group play an important role due to their widespread use and diverse cultural heritage. These languages are spoken by about one third of all speakers of the official languages of the European Union, and by over 400 million speakers worldwide. The political and economic developments in Central and Eastern Europe place societies where Balto-Slavic languages are spoken at the center of rapid technological advancement and the growing European consumer markets.On the other hand, research on theoretical and applied NLP in some of these languages still lags behind the "major" languages, such as English and other West European languages. In comparison to English, which has dominated the digital world since the advent of the Internet, many of these languages still lack resources, processing tools and applications-especially those with smaller speaker bases.The Balto-Slavic languages pose a wealth of fascinating scientific challenges. The linguistic phenomena specific to the Balto-Slavic languages-complex morphology and free word order-present non-trivial problems for construction of NLP tools, and require rich morphological and syntactic resources. This view is also reflected in Serge Sharoff's invited talk on "Pan-Slavic NLP." In the talk, he discusses an ambitious project on language adaptation-ways to adapt tools and resources among closely related languages, such as those in the Slavic group.The BSNLP Workshops aim to bring together academic researchers and industry specialists in NLP for Balto-Slavic languages. We aim to stimulate research and to foster the creation and dissemination of tools and resources. The Workshop serves as a forum for exchange of ideas and experience and for discussing shared problems. One fascinating aspect of this group of languages is their structural similarity, as well as an easily recognizable lexical and inflectional inventory spanning the entire group, which-despite the lack of mutual intelligibility-creates a special environment in which researchers can fully appreciate the shared problems and solutions.As a result of discussions at the previous BSNLP Workshops, to help catalyze collaboration, this year we have organized the first SIGSLAV Challenge: a shared task on multilingual named entity recognition. We have built a dataset, which allows systems to be evaluated on recognizing mentions of named entities in Web documents, their normalization/lemmatization, and cross-lingual matching. The Challenge initially covers seven Slavic languages, and it is intended as a first version of an evaluation standard to be expanded in the future.We received 24 regular submissions, 14 of which were accepted for presentation.The papers cover a wide range of topics. Two papers relate to lexical semantics, four to development of linguistic resources, and four to information filtering, information retrieval, and information extraction. Four papers cover topics related to...

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“…Most of these efforts begin with a set of documents known to cover a type of event or domain, then cluster verb arguments to determine each verb's role slots within that domain (Filatova et al, 2006;Sekine, 2006). These approaches typically learn verb-specific roles, rather than multi-verb event schemas.…”

Section: Related Workmentioning

confidence: 99%

Inducing Event Types and Roles in Reverse: Using Function to Discover Theme

Ahn¹

2017

Proceedings of the Events and Stories in the News Workshop

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With growing interest in automated event extraction, there is an increasing need to overcome the labor costs of hand-written event templates, entity lists, and annotated corpora. In the last few years, more inductive approaches have emerged, seeking to discover unknown event types and roles in raw text. The main recent efforts use probabilistic generative models, as in topic modeling, which are formally concise but do not always yield stable or easily interpretable results. We argue that event schema induction can benefit from greater structure in the process and in linguistic features that distinguish words' functions and themes. To maximize our use of limited data, we reverse the typical schema induction steps and introduce new similarity measures, building an intuitive process for inducing the structure of unknown events.

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Automatic creation of domain templates

Cited by 28 publications

References 23 publications

Towards Never Ending Language Learning for Morphologically Rich Languages

Towards Never Ending Language Learning for Morphologically Rich Languages

Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

Inducing Event Types and Roles in Reverse: Using Function to Discover Theme

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