Chinese information processing is a critical step toward cognitive linguistic applications like machine translation. Lexical hyponymy relation, which exists in some Eastern languages like Chinese, is a kind of hyponymy that can be directly inferred from the lexical compositions of concepts, and of great importance in ontology learning. However, a key problem is that the lexical hyponymy is so c...

Automatic acquisition of semantic relations is an important problem in knowledge acquisition. We present a method of spatial structure analysis of candidate Chinese hyponymy based on concept space and use them to identify candidate hyponymy. Firstly a group of candidate hyponymy relations is imported into concept space. Secondly we analyze the basic spatial structure of concept space including ...

In this paper we demonstrate methods of improving both the recall and the precision of automatic methods for extraction of hyponymy (IS A) relations from free text. By applying latent semantic analysis (LSA) to filter extracted hyponymy relations we reduce the rate of error of our initial pattern-based hyponymy extraction by 30%, achieving precision of 58%. Applying a graph-based model of noun-...

In this paper we demonstrate methods of improving both the recall and the precision of automatic methods for extraction of hyponymy (IS A) relations from free text. By applying latent semantic analysis (LSA) to filter extracted hyponymy relations we reduce the rate of error of our initial pattern-based hyponymy extraction by 30%, achieving precision of 58%. Applying a graph-based model of noun-...

This paper proposes a co-training style algorithm called Co-STAR that acquires hyponymy relations simultaneously from structured and unstructured text. In CoSTAR, two independent processes for hyponymy relation acquisition – one handling structured text and the other handling unstructured text – collaborate by repeatedly exchanging the knowledge they acquired about hyponymy relations. Unlike co...

Concept hierarchy knowledge, such as hyponymy and meronymy, is very important for various Natural Language Processing systems. While WordNet and Wikipedia are being manually constructed and maintained as lexical ontologies, many researchers have tackled how to extract concept hierarchies from very large corpora of text documents such as the Web not manually but automatically. However, their met...

This paper describes a method for extracting a large set of hyponymy relations from Wikipedia. The Wikipedia is much more consistently structured than generic HTML documents, and we can extract a large number of hyponymy relations with simple methods. In this work, we managed to extract more than 1.4 × 106 hyponymy relations with 75.3% precision from the Japanese version of the Wikipedia. To th...

This paper proposes an extension of Sumida and Torisawa’s method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). We extract hyponymy relation candidates (HRCs) from the hierachical layouts in Wikipedia by regarding all subordinate items of an item x in the hierachical layouts as x’s hyponym candidates, while Sumida and Torisawa (2008) extracted...