Automatically extracting Translation Links using a wide coverage semantic taxonomy

TGE (Tlink Generator Environment) is a system for semi-automatically extracting translation links. The system was developed within the ACQUILEX II2 project as a tool for supporting the construction of a multi-lingual lexical knowledge base containing detailed syntactic and semantic information from MRD resources. A drawback of the original system was the need of human intervention for selecting the more appropriate translation links in the case where more than one were extracted and proposed by the system. This paper deals with the task of overcoming this drawback. What is presented is an heuristic method based on conceptual distance that uses information from an external wide-coverage semantic taxonomy (WordNet). Our aim is to overcome the problem in an automatic way or to provide the user with complementary information in order to make his/her choice easier. 1 Motivation and introduction The research reported here is part of the ACQUILEX II project which has as one aim the automatic or semi-automatic construction of fragments of a multi-lingual lexical knowledge base containing detailed syntactic and semantic information from MRD resources. Lexical information acquisition is generally considered as a major ‘bottleneck’ in NLP. It is clear that techniques which either partially or totally automate this process should be investigated. The use of monolingual machine-readable versions of conventional dictionaries (MRDs) in the acquisition of lexical knowledge has been widely extended because they provide substantial quantities of lexical information that can be extracted with limited difficulty and limited human intervention [Dolan et al. 93]. But less attention have been paid to bilingual MRDs. [Tanaka & Umemura 94] use a third language to construct a bilingual dictionary and [Knight & Luk 94] explains a simple approach to connect Spanish nouns extracted from a bilingual dictionary to a large-scale knowledge base. During the early steps of ACQUILEX3 project the aims were to build semi-automatically large scale monolingual lexicons represented as Lexical Knowledge Bases (LKB) from lexical databases (LDB) automatically extracted from MRDs (see [Ageno et al. 92], [Copestake 92]). In such a way, lexicons covering restricted domains (food, drink, .) were built for four Languages (Dutch, English, Italian and Spanish). Further on we concentrated on using the LKB to represent multilingual information in the form of links between monolingual lexical entries, which we refer to as tlinks (translation links). and on developing a methodology for constructing tlinks from the available MRD resources. The fundamentals of the system, as well as the representational issues, can be seen in [Copestake et al. 94] and [Copestake et 1This researcher has been suported by a grant of the Ministerio de Educacion y Ciencia, 92-BOE-16392. 2AcquilexII EC Esprit project BRA 7315. 3Acquilex EC Esprit project BRA 3030 al. 92]. The description of a software environment, Tlink Generation Environment (TGE), supporting the extraction methodology can be seen in [Ageno et al. 94]. Several experiments have been carried out following this methodology and supported by TGE. The results have been summarised, reported and commented in [Copestake et al. 94] and [Ageno et al. 94]. The core of our methodology is the use of bilingual dictionaries as a main Knowledge Source for extracting tlinks. The fitness of the extracted tlinks depends heavily on the quality and coverage of such dictionaries. One of the drawbacks of the system was the need of huge specialised human intervention for selecting the more appropriate tlink in the case where more than on was allowable. The present paper makes a proposal for avoiding this intervention or for providing the user with complementary information in order to make easier his/her choice. The proposal is based on the use of a conceptual distance between the alternatives. The base for computing this distance is the use of an external widecoverage semantic taxonomy for English (WordNet). The organisation of this document is the following: After this introduction, in section 2, a short description of the underlying concepts and methodology is presented as well as a summary of the results obtained from their application. Section 3 will introduce our proposal of conceptual distance. On section 4 the way this concept can be applied for overcoming the drawbacks detected in section 2 is presented. The results of an experiment applying these ideas are presented too. Finally, some conclusions are stated in section 5.