@inproceedings{schwartz-etal-2003-disambiguation,
title = "Disambiguation of {E}nglish {PP} attachment using multilingual aligned data",
author = "Schwartz, Lee and
Aikawa, Takako and
Quirk, Chris",
booktitle = "Proceedings of Machine Translation Summit IX: Papers",
month = sep # " 23-27",
year = "2003",
address = "New Orleans, USA",
url = "https://aclanthology.org/2003.mtsummit-papers.44/",
abstract = "Prepositional phrase attachment (PP attachment) is a major source of ambiguity in English. It poses a substantial challenge to Machine Translation (MT) between English and languages that are not characterized by PP attachment ambiguity. In this paper we present an unsupervised, bilingual, corpus-based approach to the resolution of English PP attachment ambiguity. As data we use aligned linguistic representations of the English and Japanese sentences from a large parallel corpus of technical texts. The premise of our approach is that with large aligned, parsed, bilingual (or multilingual) corpora, languages can learn non-trivial linguistic information from one another with high accuracy. We contend that our approach can be extended to linguistic phenomena other than PP attachment."
}
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<abstract>Prepositional phrase attachment (PP attachment) is a major source of ambiguity in English. It poses a substantial challenge to Machine Translation (MT) between English and languages that are not characterized by PP attachment ambiguity. In this paper we present an unsupervised, bilingual, corpus-based approach to the resolution of English PP attachment ambiguity. As data we use aligned linguistic representations of the English and Japanese sentences from a large parallel corpus of technical texts. The premise of our approach is that with large aligned, parsed, bilingual (or multilingual) corpora, languages can learn non-trivial linguistic information from one another with high accuracy. We contend that our approach can be extended to linguistic phenomena other than PP attachment.</abstract>
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%0 Conference Proceedings
%T Disambiguation of English PP attachment using multilingual aligned data
%A Schwartz, Lee
%A Aikawa, Takako
%A Quirk, Chris
%S Proceedings of Machine Translation Summit IX: Papers
%D 2003
%8 sep 23 27
%C New Orleans, USA
%F schwartz-etal-2003-disambiguation
%X Prepositional phrase attachment (PP attachment) is a major source of ambiguity in English. It poses a substantial challenge to Machine Translation (MT) between English and languages that are not characterized by PP attachment ambiguity. In this paper we present an unsupervised, bilingual, corpus-based approach to the resolution of English PP attachment ambiguity. As data we use aligned linguistic representations of the English and Japanese sentences from a large parallel corpus of technical texts. The premise of our approach is that with large aligned, parsed, bilingual (or multilingual) corpora, languages can learn non-trivial linguistic information from one another with high accuracy. We contend that our approach can be extended to linguistic phenomena other than PP attachment.
%U https://aclanthology.org/2003.mtsummit-papers.44/
Markdown (Informal)
[Disambiguation of English PP attachment using multilingual aligned data](https://aclanthology.org/2003.mtsummit-papers.44/) (Schwartz et al., MTSummit 2003)
ACL