used in a particular domain and provide valuable knowledge for a range of natu-

ral language processing applications. However, for many domains and languages

ontologies do not exist and manual creation is a difficult and resource-intensive

process. As such, automatic methods to extract, expand or aid the construction

of these resources is of significant interest.

There are a number of methods for extracting semantic information about

how terms are related from raw text, most notably the approach of Hearst

[1992], who used

manual and it is not clear how to automatically generate patterns, which are

specific to a given relationship and domain. I present a novel method for de-

veloping patterns based on the use of alignments between patterns. Alignment

works well as it is closely related to the concept of a

minimally generalise over-fitting patterns. I show that join-sets can be viewed

as an reduction on the search space of patterns, while resulting in no loss of

accuracy. I then show the results can be combined by a

to a obtain a classifier, which can decide if a pair of terms are related. I applied

this to several data sets and conclude that this method produces a precise result,

with reasonable recall.

The system I developed, like many semantic relation systems, produces only

a binary decision of whether a term pair is related. Ontologies have a structure,

that limits the forms of networks they represent. As the relation extraction is

generally noisy and incomplete, it is unlikely that the extracted relations will

match the structure of the ontology. As such I represent the structure of ontol-

ogy as a set of logical statements, and form a consistent ontology by finding the

network closest to the relation extraction system's output, which is consistent

with these restrictions. This gives a novel

which I develop several algorithms. I present simple greedy approaches, and

branch and bound approaches, which my results show are not sufficient for this

problem. I then use resolution to show how this problem can be stated as an

a

problem, and furthermore when applied to the result of the relation extraction

system, this improves the quality of the extraction as well as converting it to an

ontological structure., application/pdf, 総研大甲第1288号}, title = {AUTOMATIC EXTRACTION OF LOGICALLY CONSISTENT ONTOLOGIES FROM TEXT CORPORA}, year = {} }