Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences

Kentaro Inui; Toru Hirano; Ryu Iida; Atsushi Fujita; Yuji Matsumoto

Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences

Kentaro Inui, Toru Hirano, Ryu Iida, Atsushi Fujita, Yuji Matsumoto

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Abstract

One of the crucial issues in semantic parsing is how to reduce costs of collecting a sufficiently large amount of labeled data. This paper presents a new approach to cost-saving annotation of example sentences with predicate-argument structure information, taking Japanese as a target language. In this scheme, a large collection of unlabeled examples are first clustered and selectively sampled, and for each sampled cluster, only one representative example is given a label by a human annotator. The advantages of this approach are empirically supported by the results of our preliminary experiments, where we use an existing similarity function and naive sampling strategy.

Anthology ID:: L06-1370
Volume:: Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
Month:: May
Year:: 2006
Address:: Genoa, Italy
Editors:: Nicoletta Calzolari, Khalid Choukri, Aldo Gangemi, Bente Maegaard, Joseph Mariani, Jan Odijk, Daniel Tapias
Venue:: LREC
SIG:
Publisher:: European Language Resources Association (ELRA)
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Pages:
Language:
URL:: http://www.lrec-conf.org/proceedings/lrec2006/pdf/610_pdf.pdf
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Cite (ACL):: Kentaro Inui, Toru Hirano, Ryu Iida, Atsushi Fujita, and Yuji Matsumoto. 2006. Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).
Cite (Informal):: Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences (Inui et al., LREC 2006)
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PDF:: http://www.lrec-conf.org/proceedings/lrec2006/pdf/610_pdf.pdf

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@inproceedings{inui-etal-2006-augmenting,
    title = "Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences",
    author = "Inui, Kentaro  and
      Hirano, Toru  and
      Iida, Ryu  and
      Fujita, Atsushi  and
      Matsumoto, Yuji",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Gangemi, Aldo  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Odijk, Jan  and
      Tapias, Daniel",
    booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
    month = may,
    year = "2006",
    address = "Genoa, Italy",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L06-1370/",
    abstract = "One of the crucial issues in semantic parsing is how to reduce costs of collecting a sufficiently large amount of labeled data. This paper presents a new approach to cost-saving annotation of example sentences with predicate-argument structure information, taking Japanese as a target language. In this scheme, a large collection of unlabeled examples are first clustered and selectively sampled, and for each sampled cluster, only one representative example is given a label by a human annotator. The advantages of this approach are empirically supported by the results of our preliminary experiments, where we use an existing similarity function and naive sampling strategy."
}

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%0 Conference Proceedings
%T Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences
%A Inui, Kentaro
%A Hirano, Toru
%A Iida, Ryu
%A Fujita, Atsushi
%A Matsumoto, Yuji
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F inui-etal-2006-augmenting
%X One of the crucial issues in semantic parsing is how to reduce costs of collecting a sufficiently large amount of labeled data. This paper presents a new approach to cost-saving annotation of example sentences with predicate-argument structure information, taking Japanese as a target language. In this scheme, a large collection of unlabeled examples are first clustered and selectively sampled, and for each sampled cluster, only one representative example is given a label by a human annotator. The advantages of this approach are empirically supported by the results of our preliminary experiments, where we use an existing similarity function and naive sampling strategy.
%U https://aclanthology.org/L06-1370/

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Markdown (Informal)

[Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences](https://aclanthology.org/L06-1370/) (Inui et al., LREC 2006)

Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences (Inui et al., LREC 2006)

ACL

Kentaro Inui, Toru Hirano, Ryu Iida, Atsushi Fujita, and Yuji Matsumoto. 2006. Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).