Extracting hyponyms of prespecified hypernyms from itemizations and headings in web documents

Shinzato, Keiji; Torisawa, Kentaro

doi:10.3115/1220355.1220490

Cited by 10 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As seen in previous studies [20,21], hyponymy relations are likely to appear in tables. Likewise, we also expect them to appear in that of statutes.…”

Section: Extraction Of Hyponymy Relations From Tablesmentioning

confidence: 82%

“…Shinzato et al [20,21] proposed a method to acquire hyponyms for given hypernyms from HTML documents, assuming that the heading of an itemization in an HTML document is likely to contain a hypernym of the items in the itemization. For an itemization in the HTML format that corresponds to a table in a statute, a similar method could be applied to the statutes as long as a statute is described in a structural format, such as XML.…”

Section: Previous Work On Legal Text Processingmentioning

confidence: 99%

See 1 more Smart Citation

Acquisition of hyponymy relations for agricultural terms from a Japanese statutory corpus

Nakamura

Ohno

Ogawa

et al. 2014

Information Processing in Agriculture

View full text Add to dashboard Cite

Legal text processingA B S T R A C T This paper, which aims to increment the vocabulary of an existing thesaurus using hyponymy relations, focuses on an agricultural thesaurus called AGROVOC. Our main goal is to acquire AGROVOC-qualified candidates from the hyponymy relations of legal texts and tables. We propose a pattern-based approach to hyponymy relation acquisition. Our experimental result showed that 222 and 868 candidates are extracted from statutory sentences with 67.1% precision and tables with 37.0% precision, respectively.

show abstract

“…As seen in previous studies [20,21], hyponymy relations are likely to appear in tables. Likewise, we also expect them to appear in that of statutes.…”

Section: Extraction Of Hyponymy Relations From Tablesmentioning

confidence: 82%

Section: Previous Work On Legal Text Processingmentioning

confidence: 99%

Acquisition of hyponymy relations for agricultural terms from a Japanese statutory corpus

Nakamura

Ohno

Ogawa

et al. 2014

Information Processing in Agriculture

View full text Add to dashboard Cite

show abstract

“…Moreover, as discussed previously, the semantic concepts specified in many domain ontologies are structured only in the subsumption manner of super-class and sub-class, rather than the more specific is-a, part-of, and related-to, the ones developed by [31,46] and [136]. Some attempted to describe more specified relations, like [13,103] for is-a, [33,92] for part-of, and [41] for related-to relations only. Tao et al [107,108] made a further progress from these works and portrayed the basic is-a, part-of, and relatedto semantic relations in one single computational model for concept representation.…”

Section: Semantic Concept Representationmentioning

confidence: 99%

Personalised Information Gathering and Recommender Systems: Techniques and Trends

Tao¹,

Zhou²,

Lau³

et al. 2013

ICST Trans ScaInfoSys

View full text Add to dashboard Cite

With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-the-art tools for efficient selection of the most relevant and reliable information resources, and the interest in such systems has increased dramatically over the last few years. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender systems from a technological and social perspective. Aiming to promote high quality research in order to overcome these challenges, this paper provides a comprehensive survey on the recent work and achievements in the areas of personalised web information gathering and recommender systems. The report covers concept-based techniques exploited in personalised information gathering and recommender systems.

show abstract

“…Some research [16][17] tried to create a categorized vocabulary dictionary by using web mining. For example, one category is "programming language" and the vocabularies are "Java", "C", "Perl" and so on.…”

Section: Named Entity Extractionmentioning

confidence: 99%

A search result clustering method using informatively named entities

Toda

Kataoka

2005

Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management

View full text Add to dashboard Cite

Clustering the results of a search helps the user to overview the information returned. In this paper, we regard the clustering task as indexing the search results. Here, an index means a structured label list that can makes it easier for the user to comprehend the labels and search results. To realize this goal, we make three proposals. First is to use Named Entity Extraction for term extraction. Second is a new label selecting criterion based on importance in the search result and the relation between terms and search queries. The third is label categorization using category information of labels, which is generated by NE extraction. We implement a prototype system based on these proposals and find that it offers much higher performance than existing methods; we focus on news articles in this paper.

show abstract

Extracting hyponyms of prespecified hypernyms from itemizations and headings in web documents

Cited by 10 publications

References 6 publications

Acquisition of hyponymy relations for agricultural terms from a Japanese statutory corpus

Acquisition of hyponymy relations for agricultural terms from a Japanese statutory corpus

Personalised Information Gathering and Recommender Systems: Techniques and Trends

A search result clustering method using informatively named entities

Contact Info

Product

Resources

About