Phonetic name matching for cross-lingual Spoken Sentence Retrieval

Ji, Heng; Grishman, Ralph; Wang, Wen

doi:10.1109/slt.2008.4777895

2008 IEEE Spoken Language Technology Workshop 2008

DOI: 10.1109/slt.2008.4777895

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Phonetic name matching for cross-lingual Spoken Sentence Retrieval

Heng Ji

Ralph Grishman

Wen Wang

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Cited by 3 publications

References 10 publications

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Web Crawler System for Distinct Author Identification in Bibliographic Databases

Dau¹,

Russo²,

Bouwsema³

et al. 2015

The 7th International Conference on Information Technology

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A person's name is regularly used to uniquely identify himself/herself from others; unfortunately names are in no way unique and this leads to serious problems. For instance, when trying to retrieve papers from academic database repositories, it can be difficult to distinguish one author from another if the individuals in question have the exact same name. An author can also assume another name, for instance by using the full name. Thus, being able to differentiate which person a specific name is referring to can be tricky. In this paper, we propose a method to solve this ambiguity problem by gathering information from bibliographic databases and using this information to create a social network tree. Based on the relationships created among co-authors it is possible to disambiguate authors with a high-level of accuracy.

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Web Crawler System for Distinct Author Identification in Bibliographic Databases

Dau¹,

Russo²,

Bouwsema³

et al. 2015

The 7th International Conference on Information Technology

View full text Add to dashboard Cite

show abstract

Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages

Blissett¹,

Ji²

2019

Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

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In this paper we address a challenging crosslingual name retrieval task. Given an English named entity query, we aim to find all name mentions in documents in low-resource languages. We present a novel method which relies on zero annotation or resources from the target language. By leveraging freely available, cross-lingual resources and a small amount of training data from another language, we are able to perform name retrieval on a new language without any additional training data. Our method proceeds in a multi-step process: first, we pretrain a language-independent orthographic encoder using Wikipedia inter-lingual links from dozens of languages. Next, we gather user expectations about important entities in an English comparable document and compare those expected entities with actual spans of the target language text in order to perform name finding. Our method shows 11.6% absolute Fscore improvement over state-of-the-art methods.

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Mining name translations from comparable corpora by creating bilingual information networks

2009

Proceedings of the 2nd Workshop on Building and Using Comparable Corpora From Parallel to Non-Parallel Corpora - BUCC '09

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This paper describes a new task to extract and align information networks from comparable corpora. As a case study we demonstrate the effectiveness of this task on automatically mining name translation pairs. Starting from a small set of seeds, we design a novel approach to acquire name translation pairs in a bootstrapping framework. The experimental results show this approach can generate highly accurate name translation pairs for persons, geopolitical and organization entities.

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Phonetic name matching for cross-lingual Spoken Sentence Retrieval

Cited by 3 publications

References 10 publications

Web Crawler System for Distinct Author Identification in Bibliographic Databases

Web Crawler System for Distinct Author Identification in Bibliographic Databases

Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages

Mining name translations from comparable corpora by creating bilingual information networks

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