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AGGREGATION
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Gracheva, Varya; Crowgey, Joshua; Bender, Emily M.; O'Hara, Kelly; Wax, David; Zamaraeva, Olga; Ramaswamy, Swetha; Curtis, Chris; Oepen, Stephan; Flickinger, Dan; Bateman, Emma; Nordlinger, Rachel; Song, Sanghoun; Zhang, Claude; Lepp, Haley; Dermer, Laurie; Goodman, Michael Wayne; Howell, Kristen; Packard, Woodley; Lockwood, Michael Wayne; Fokkens, Antske; Hou, Joshua; Haeger, Mike; Burrel, Alex; Mills, Daniel P.; Halgrim, Scott; Heath, Jeff; Poulson, Laurie; Nielsen, Elizabeth; Drellishak, Scott; Inman, David; Xia, Fei; Trimble, TJ. - 2020
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Abstract:
This archive is associated with the AGGREGATION project, which seeks to automatically generate HPSG grammars on the basis of Interlinnear Glossed Text data. For a detailed description of this project see Chapter 3 of Inferring Grammars from Interlinear Glossed Text: Extracting Typological and Lexical Properties for the Automatic Generation of HPSG Grammars, PhD thesis by Kristen Howell 2020. This archive includes the following: The AGGREGATION/BASIL syntactic inference repository from https://git.ling.washington.edu/agg/aggregation The MOM morphological inference repository from https://git.ling.washington.edu/agg/mom The Xigt framework for eXtensible Interlinear Glossed Text release 1.1 from https://github.com/xigt/xigt The Grammar Matrix Customization system http://matrix.ling.washington.edu/index.html Code, dependencies and sample data for running the AGGREGATION pipeline end to end. ; The AGGREGATION Project aims to bring the benefits of grammar engineering to language documentation without requiring field linguists to become grammar engineers. We achieve this by automatically creating precision grammars on the basis of analyses and annotations already produced by field linguists together with a typologically-grounded cross-linguistic grammar resource (the LinGO Grammar Matrix) and natural language processing techniques developed for high-resource languages. Precision grammars are machine-readable encodings of mutually-consistent linguistic hypotheses, in our case, concerning morphotactics, morphosyntax and the syntax-semantics interface. They can be used to automatically process text, assigning structures to input strings and strings to input semantic representations. Text processed in this way can then be searched for sentences or word forms with structures of interest or items that are not covered by the grammar (i.e. fall outside current hypotheses). ; National Science Foundation under Grant No. BCS-1160274 (PI Bender) National Science Foundation under Grant No. BCS-1561833 (PI Bender)
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Keyword:
Endangered Languages; Grammar Engineering; Grammar Inference; HPSG Syntax; Interlinear Glossed Text; Typology
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URL: http://hdl.handle.net/1773/46233
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Linguistic fundamentals for natural language processing II: 100 essentials from semantics and pragmatics
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Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task ...
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STREAMLInED Challenges: Aligning Research Interests with Shared Tasks
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In: 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages, March 6-7, 2017. Honolulu, Hawai‘i, USA (2017)
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From Database to Treebank: On Enhancing Hypertext Grammars with Grammar Engineering and Treebank Search
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From Database to Treebank: On Enhancing Hypertext Grammars with Grammar Engineering and Treebank Search
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