It is well known that the most nagging issue for word sense disambiguation (WSD) is the definition of just what a word sense is. At its base, the problem is a philosophical and linguistic one that is far from being resolved. However, work in automated language processing has led to efforts to find practical means to distinguish word senses, at least to the degree that they are useful for natural language processing tasks such as summarization, document retrieval, and machine translation. Several criteria have been suggested and exploited to automatically determine the sense of a word in context, including syntactic behavior, semantic and pragmatic knowledge, and especially in more recent empirical studies, word co-occurrence within syntactic relations (e.g., Hearst, 1991; Yarowsky, 1993), words co-occurring in global context (e.g., Gale et al., 1993; Yarowsky, 1992; Schütze, 1992, 1993), etc. No clear criteria have emerged, however, and the problem continues to loom large for WSD work.
The notion that cross-lingual comparison can be useful for sense disambiguation has served as a basis for some recent work on WSD. For example, Brown et al. (1991) and Gale et al. (1992a, 1993) used the parallel, aligned Hansard Corpus of Canadian Parliamentary debates for WSD, and Dagan et al. (1991) and Dagan and Itai (1994) used monolingual corpora of Hebrew and German and a bilingual dictionary. These studies rely on the assumption that the mapping between words and word senses varies significantly among languages. For example, the word duty in English translates into French as devoir in its obligation sense, and impôt in its tax sense. By determining the translation equivalent of duty in a parallel French text, the correct sense of the English word is identified. These studies exploit this information in order to gather co-occurrence data for the different senses, which is then used to disambiguate new texts. In related work, Dyvik (1998) used patterns of translational relations in an English-Norwegian parallel corpus (ENPC, Oslo University) to define semantic properties such as synonymy, ambiguity, vagueness, and semantic fields and suggested a derivation of semantic representations for signs (e.g., lexemes), capturing semantic relationships such as hyponymy etc., from such translational relations.
Recently, Resnik and Yarowsky (1997) suggested that for the purposes of WSD, the different senses of a word could be determined by considering only sense distinctions that are lexicalized cross-linguistically. In particular, they propose that some set of target languages be identified, and that the sense distinctions to be considered for language processing applications and evaluation be restricted to those that are realized lexically in some minimum subset of those languages. This idea would seem to provide an answer, at least in part, to the problem of determining different senses of a word: intuitively, one assumes that if another language lexicalizes a word in two or more ways, there must be a conceptual motivation. If we look at enough languages, we would be likely to find the significant lexical differences that delimit different senses of a word.
However, this suggestion raises several questions. For instance, it is well known that many ambiguities are preserved across languages (for example, the French intérêt and the English interest), especially languages that are relatively closely related, such as English and French. Assuming this problem can be overcome, should differences found in closely related languages be given lesser (or greater) weight than those found in more distantly related languages? More generally, which languages should be considered for this exercise? All languages? Closely related languages? Languages from different language families? A mixture of the two? How many languages would be "enough" to provide adequate information for this purpose?
There is also the question of the criteria that would be used to establish that a sense distinction is "lexicalized cross-linguistically". How consistent must the distinction be? Does it mean that two concepts are expressed by mutually non-interchangeable lexical items in some significant number of other languages, or need it only be the case that the option of a different lexicalization exists in a certain percentage of cases?
This paper attempts to provide some preliminary answers to these questions, in order to eventually determine the degree to which the use of parallel data is viable to determine sense distinctions, and if so, the ways in which this information might be used. Given the lack of large parallel texts across multiple languages, the study is necessarily limited; however, close examination of a small sample of parallel data can, as a first step, provide the basis and direction for more extensive studies. I have used parallel, aligned versions of George Orwell's Nineteen Eighty-Four (Erjavec and Ide, 1998) in five languages: English, Slovene, Estonian, Romanian, and Czech. The study therefore involves languages from four language families (Germanic, Slavic, Finno-Ugrec, and Romance), as well as two languages from the same family (Czech and Slovene). Four ambiguous English words were considered in this study: hard, line, head, and country. Line and hard were chosen because they have served in various WSD studies to date (e.g., Leacock et al, 1993) and a corpus of occurrences of these words from the Wall Street Journal corpus was generously made available for comparison. Serve, another word frequently used in these studies, did not appear frequently enough in the Orwell text to be considered, nor did any other suitable ambiguous verb. Country and head were chosen as substitutes because they appeared frequently enough for consideration.
All sentences containing an occurrence or occurrences (including morphological variants) of each of the three words were extracted from the English text, together with the parallel sentences in which they occur in the texts of the four comparison languages (Czech, Estonian, Romanian, Slovene). The English occurrences were grouped into senses, using the relatively coarse sense distinctions in the Oxford Advanced Learner's Dictionary (OALD) (used to provide sense distinctions in WordNet [Miller et al., 1990; Fellbaum, forthcoming]). The sense categorization was performed by the author and two student assistants; results from the three were compared and a final, mutually agreeable grouping was established.
For each of the four comparison languages, the corpus of sense-grouped parallel sentences for English and that language was sent to a linguist and native speaker of the comparison language. The linguists were asked to provide the following information for each word occurrence:
For over 85% of the English word occurrences, a specific lexical item or items could be identified as the translation equivalent for the corresponding English word.
In order to determine the degree to which the assigned sense distinctions from the OALD correspond to translation equivalents, a coherence index (CI) was computed that measures the consistency with which a given sense is translated as well as the degree of relationship between two senses. CIs were also computed for each language individually. A simple correlation was then run over all the CI data, providing an index of the degree to which there is an affinity (or disaffinity) between the different senses of a given word in each of these groups. Finally, in order to determine the degree to which the linguistic relation between languages may affect coherence, a correlation was run among CIs for all pairs of the four target languages.
The results, which will be fully outlined in the final paper, show that cross-lingual information may be useful for automatic disambiguation, especially for gross sense distinctions. More interestingly, the data suggest that translation equivalents may provide a valuable source of information for determining sense distinctions, by providing an indication of the relatedness between sense s of a given word that are derived from standard dictionaries. In particular, the CI's can be used to define a "map" depicting the relative distance between senses. This information can in turn serve as a basis for collapsing two senses when the cross-lingual data suggest that their meanings are nearly interchangeable or for recognizing them as relatively distinct. An even more promising approach for sense determination is to use information about translation equivalents to group word occurrences into sense clusters without assigning or considering senses defined a priori in a dictionary. We are exploring this last idea and will have additional results to report in the final paper.