Any-language frame semantic parsing

Anders Trærup Johannsen; Hector Martinez Alonso; Anders Søgaard

Any-language frame semantic parsing

Anders Trærup Johannsen, Hector Martinez Alonso, Anders Søgaard

Centre for Language Technology

8 Citations (Scopus)

Abstract

We present a multilingual corpus of Wikipedia and Twitter texts annotated with FRAMENET 1.5 semantic frames in nine different languages, as well as a novel technique for weakly supervised cross-lingual frame-semantic parsing. Our approach only assumes the existence of linked, comparable source and target language corpora (e.g., Wikipedia) and a bilingual dictionary (e.g., Wiktionary or BABELNET). Our approach uses a truly interlingual representation, enabling us to use the same model across all nine languages. We present average error reductions over running a state-of-the-art parser on word-to-word translations of 46% for target identification, 37% for frame identification, and 14% for argument identification.

Original language	English
Title of host publication	Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Number of pages	5
Place of Publication	Lisbon, Portugal
Publisher	Association for Computational Linguistics
Publication date	2015
Pages	2062-2066
ISBN (Print)	978-1-941643-32-7
Publication status	Published - 2015

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Any-language frame semantic parsing. / Johannsen, Anders Trærup; Martinez Alonso, Hector; Søgaard, Anders.

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal : Association for Computational Linguistics, 2015. p. 2062-2066.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AB - We present a multilingual corpus of Wikipedia and Twitter texts annotated with FRAMENET 1.5 semantic frames in nine different languages, as well as a novel technique for weakly supervised cross-lingual frame-semantic parsing. Our approach only assumes the existence of linked, comparable source and target language corpora (e.g., Wikipedia) and a bilingual dictionary (e.g., Wiktionary or BABELNET). Our approach uses a truly interlingual representation, enabling us to use the same model across all nine languages. We present average error reductions over running a state-of-the-art parser on word-to-word translations of 46% for target identification, 37% for frame identification, and 14% for argument identification.

M3 - Article in proceedings

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BT - Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

PB - Association for Computational Linguistics

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ER -

Any-language frame semantic parsing

Abstract

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