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        <title>Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics</title>
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            <namePart type="given">Yuji</namePart>
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    <abstract>We present experiments in incrementally learning a dependency parser. The parser will be used in the WordsEye Linguistics Tools (WELT) (Ulinski et al., 2014) which supports field linguists documenting a language’s syntax and semantics. Our goal is to make syntactic annotation faster for field linguists. We have created a new parallel corpus of descriptions of spatial relations and motion events, based on pictures and video clips used by field linguists for elicitation of language from native speaker informants. We collected descriptions for each picture and video from native speakers in English, Spanish, German, and Egyptian Arabic. We compare the performance of MSTParser (McDonald et al., 2006) and MaltParser (Nivre et al., 2006) when trained on small amounts of this data. We find that MaltParser achieves the best performance. We also present the results of experiments using the parser to assist with annotation. We find that even when the parser is trained on a single sentence from the corpus, annotation time significantly decreases.</abstract>
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%0 Conference Proceedings
%T Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics
%A Ulinski, Morgan
%A Hirschberg, Julia
%A Rambow, Owen
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F ulinski-etal-2016-incrementally
%X We present experiments in incrementally learning a dependency parser. The parser will be used in the WordsEye Linguistics Tools (WELT) (Ulinski et al., 2014) which supports field linguists documenting a language’s syntax and semantics. Our goal is to make syntactic annotation faster for field linguists. We have created a new parallel corpus of descriptions of spatial relations and motion events, based on pictures and video clips used by field linguists for elicitation of language from native speaker informants. We collected descriptions for each picture and video from native speakers in English, Spanish, German, and Egyptian Arabic. We compare the performance of MSTParser (McDonald et al., 2006) and MaltParser (Nivre et al., 2006) when trained on small amounts of this data. We find that MaltParser achieves the best performance. We also present the results of experiments using the parser to assist with annotation. We find that even when the parser is trained on a single sentence from the corpus, annotation time significantly decreases.
%U https://aclanthology.org/C16-1043
%P 440-449

Markdown (Informal)

[Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics](https://aclanthology.org/C16-1043) (Ulinski et al., COLING 2016)

• Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics (Ulinski et al., COLING 2016)

ACL

• Morgan Ulinski, Julia Hirschberg, and Owen Rambow. 2016. Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 440–449, Osaka, Japan. The COLING 2016 Organizing Committee.