@inproceedings{kanayama-etal-2024-automatic,
title = "Automatic Manipulation of Training Corpora to Make Parsers Accept Real-world Text",
author = "Kanayama, Hiroshi and
Iwamoto, Ran and
Muraoka, Masayasu and
Ohko, Takuya and
Miyamoto, Kohtaroh",
editor = {Bhatia, Archna and
Bouma, Gosse and
Do{\u{g}}ru{\"o}z, A. Seza and
Evang, Kilian and
Garcia, Marcos and
Giouli, Voula and
Han, Lifeng and
Nivre, Joakim and
Rademaker, Alexandre},
booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.mwe-1.3",
pages = "4--13",
abstract = "This paper discusses how to build a practical syntactic analyzer, and addresses the distributional differences between existing corpora and actual documents in applications. As a case study we focus on noun phrases that are not headed by a main verb and sentences without punctuation at the end, which are rare in a number of Universal Dependencies corpora but frequently appear in the real-world use cases of syntactic parsers. We converted the training corpora so that their distribution is closer to that in realistic inputs, and obtained the better scores both in general syntax benchmarking and a sentiment detection task, a typical application of dependency analysis.",
}
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%0 Conference Proceedings
%T Automatic Manipulation of Training Corpora to Make Parsers Accept Real-world Text
%A Kanayama, Hiroshi
%A Iwamoto, Ran
%A Muraoka, Masayasu
%A Ohko, Takuya
%A Miyamoto, Kohtaroh
%Y Bhatia, Archna
%Y Bouma, Gosse
%Y Doğruöz, A. Seza
%Y Evang, Kilian
%Y Garcia, Marcos
%Y Giouli, Voula
%Y Han, Lifeng
%Y Nivre, Joakim
%Y Rademaker, Alexandre
%S Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F kanayama-etal-2024-automatic
%X This paper discusses how to build a practical syntactic analyzer, and addresses the distributional differences between existing corpora and actual documents in applications. As a case study we focus on noun phrases that are not headed by a main verb and sentences without punctuation at the end, which are rare in a number of Universal Dependencies corpora but frequently appear in the real-world use cases of syntactic parsers. We converted the training corpora so that their distribution is closer to that in realistic inputs, and obtained the better scores both in general syntax benchmarking and a sentiment detection task, a typical application of dependency analysis.
%U https://aclanthology.org/2024.mwe-1.3
%P 4-13
Markdown (Informal)
[Automatic Manipulation of Training Corpora to Make Parsers Accept Real-world Text](https://aclanthology.org/2024.mwe-1.3) (Kanayama et al., MWE-UDW-WS 2024)
ACL