Projecting Corpus-Based Semantic Links on a Thesaurus

Hypernym links acquired through an information extraction procedure are projected on multi-word terms through the recognition of semantic variations. The quality of the projected links resulting from corpus-based acquisition is compared with projected links extracted from a technical thesaurus. 1 Motivation In the domain of corpus-based terminology, there are two main topics of research: term acquisition-the discovery of candidate terms-and automatic thesaurus construction-the addition of semantic links to a term bank. Several studies have focused on automatic acquisi-The output of these tools is a list of unstructured multi-word terms. On the other hand, contributions to automatic construction of thesauri provide classes or links between single words. Classes are produced by clustering techniques based on similar word contexts (Schiitze, 1993) or similar distributional contexts (Grefenstette, 1994). Links result from automatic acquisition of relevant predicative or discursive pat-Predicative patterns yield predicative relations such as cause or effect whereas discursive patterns yield non-predicative relations such as generic/specific or synonymy links. * The experiments presented in this paper were performed on [AGRO], a 1.3-million word French corpus of scientific abstracts in the agricultural domain. The ter-mer used for multi-word term acquisition is ACABIT (Daille, 1996). It has produced 15,875 multi-word terms composed of 4,194 single words. For expository purposes , some examples are taken from [MEDIC], a 1.56-million word English corpus of scientific abstracts in the medical domain. The main contribution of this article is to bridge the gap between term acquisition and thesaurus construction by offering a framework for organizing multi-word candidate terms with the help of automatically acquired links between single-word terms. Through the extraction of semantic variants, the semantic links between single words are projected on multi-word candidate terms. As shown in Figure 1, the input to the system is a tagged corpus. A partial ontology between single word terms and a set of multi-word candidate terms are produced after the first step. In a second step, layered hierarchies of multi-word terms are constructed through corpus-based conflation of semantic variants. Even though we focus here on generic/specific relations, the method would apply similarly to any other type of semantic relation. The study is organized as follows. First, the method for corpus-based acquisition of semantic links is presented. Then, the tool for semantic term normalization is described together with its application to semantic link projection. The last section analyzes the results on an agricultural corpus and evaluates the quality of the induced …