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Combining machine readable lexical resources and bilingual corpora for broad word sense disambiguation

This paper describes a new approach to word sense disambiguation (WSD) based on automatically acquired "word sense division. The semantically related sense entries in a bilingual dictionary are arranged in clusters using a heuristic labeling algorithm to provide a more complete and appropriate sense division for WSD. Multiple translations of senses serve as outside information for automatic tagging of bilingual corpora and acquisition of WSD rules. We describe and implement a WSD method using the English-Chinese bilingual version (LecDOCE) of the Longman Dictionary of Contemporary English (LDOCE). For this purpose, we draw on information about topics and topical sets in the Longman Lexicon of Contemporary English (LLOCE) to represent and disambiguate LecDOCE senses. Example sentences and their translations from LecDOCE are employed as training materials for WSD, while further examples from the Brown corpus are used for testing. Quantitative results of disambiguating 12 words are also presented.


Introduction
Word sense disambiguation (WSD) has been found useful in many NLP applications, including information retrieval (McRoy 1992; Krovetz and Croft 1992) and machine translation (Brown et al. 1991; Dagan et al. 1991; Dagan and Itai 1994). Recent work on computational linguistics have paid increasing attention to WSD (Lesk 1986;Schütze 1992; Gale et al. 1992;Yarowsky 1992 andLuk 1995). Given a polysemous word in running text, the task of WSD involves examining contextual information to determine the intended sense from a predefined set. If the set of senses is chosen to be possible translations of the word of interest, the WSD becomes the problem of lexical selection (Dagan et al. 1991; Dagan and Itai 1994) in machine translation (MT) process. WSD methods can be characterized by how word senses are divided and by how WSD knowledge is represented and acquired.
Word sense is an abstract concept frequently based on subjective and subtle distinctions in topic, register, dialect, collocation, part-of-speech and valency (McRoy 1992). Researchers have experimented with various knowledge sources for WSD system, including (1) the defining words in every-day dictionaries (Lesk 1986;Cowie et al. 1992;Luk 1995), (2) indicative words in the context of words listed under a thesaurus category acquired from corpus (Yarowsky 1992; Chen and Chang 1994), (3) bilingual corpora or monolingual corpora in the target language (Gale, Church and Yarowsky 1992; Dagan et al. 1991; and Itai 1994), (4) automatic induced clusters with sublexical representation (Schütze 1992), and (5) handcrafted lexicon containing knowledge from multiple sources (McRoy 1992). This study is motivated by several observations. First, word-based approaches trained on dictionaries or corpora offer limited coverage for unrestricted text. Lesk (1986) described a word-sense disambiguation technique that is based on the number of overlap between words in a dictionary definition and words in the local context of the word to be disambiguated. The author reported that WSD performance ranged from 50 to 70%. On the other hand, the performance of corpus-trained word-based models (Yarowsky 1992 andGale, Church and Yarowsky 1992) were shown to be very effective. However, Luk (1995) pointed out that these methods were tested on technical writing or text in a limited domain, therefore it remains doubtful whether these models can perform as well for text with all kinds of genre such as the Brown corpus.
With a set of programs described in this paper, we try to find out whether it is possible to exploit the existing machine readable lexical resources to put to use in automatic acquisition of knowledge bases of word sense division and disambiguation. Furthermore, we also experimented with the concept-based approach to see if it can provide broader coverage, while maintaining comparable high precision. We hope to achieve a degree of generality, so it is possible to resolve word sense ambiguity, even for a word found in a particular unfamiliar context in terms of word overlapping. For instance, the sense of "bank" in the context of "vole" (see Table 1) is arguably difficult to disambiguate, even with a very large training corpus. Consider the sentences that contains the word "bank" among some 25,000 example sentences in LecDOCE (Proctor 1988). The intended sense of "bank" in these sentences is predominately FINANCE. Table 1 provides further details for other sentences which are related to the GEOGRAPHY sense of "bank," taking note of the indicative context and the Chinese translation of "bank." First, observe that this sample of sentential context for the GEOGRAPHY sense of "bank" is so small that it does not include a whole lot of re-occurring words, except for the word "river" Therefore, it is very difficult to guarantee broad coverage, when disambiguation is dependent on word overlaps. However, it is evident that even with a sample as small as this, there are many re-occurring topics or concepts such as GEOGRAPHY ("stream," "sea," "lake," "earth," "flood," "water," "woods," "hill," "river," "to flow," and "to overflow"), DIRECTION ("north," "south," "east," and "west"), POSITION ("left," "right," and "side"), and ANIMALS ("vole" and "deer"). The data seems to indicate that a class-based approach will be effective for WSD.
Second, the translations are quite diversified; six distinct translations for ten instances of "bank" The rest of the paper is organized as follows. We begin by giving the details of material used, including the characteristics of definition sentences in LecDOCE and the organization of words in LLOCE. Next, a set of four algorithms for (1) labeling LecDOCE senses, (2) tagging bilingual corpora, (3) acquisition of WSD rules, and (4) rule-based WSD, are described. Examples demonstrating the effectiveness of the algorithms are given for illustration. After describing the algorithm, the experimental results for a twelve-word test set are presented. Moreover, the proposed method is compared with other approaches in computational linguistics literature. Finally, concluding remarks are made.

Labeling dictionary senses
The labeling of dictionary definition sentences with a coarse sense distinction like the set labels in LLOCE is a special form of the WSD problem. No simple method can solve the general problem of WSD in unrestricted text. We will show that this labeling task is made simpler for several reasons. For instance, consider the definition sentences for the first few senses of "bank" in LecDOCE as shown in Table 1.
First of all, only simple words are used in the definitions. Furthermore, the text generation schemes are rather regular. The scheme that lexicographers used in generating above definitions is similar to the DEFINITION scheme described in McKeown (1985). A DEFINITION scheme begins with a genus term (i.e., conceptual parent or ancestor of the sense), followed by the so-called differentia which consists of words semantically related to the sense to provide specifics about the sense. Those relations have been shown to be very effective knowledge sources for WSD (McRoy 1992). For the most part, those relations exist conveniently among words under the same topic or across topics of cross reference in LLOCE. For instance, most of the above mentioned words are listed under the same topic Ld (Geography) of intended label Ld099, or its cross reference Me (Places). Therefore, these definitions can be disambiguated very effectively on the base of similarity between the defining keywords and the words lists in LLOCE.

Discussion
Work in sense disambiguation can be categorized by the way word senses are represented and how senseindicative factors are acquired and recorded. Recently, researchers have turned to machine readable dictionaries (MRD) in order to save labor-intensive effort in representing word sense and WSD knowledge (McRoy 1992). The sense number in the on-line LDOCE dictionary has been used to represent word senses (Lesk 1986). This approach has the advantage of having definition and example sentences explicitly associated with a certain word sense, so proto-typical words in the indicative context of a certain word sense can obtained directly. However, as noted by Dolan (1994), dictionary dichotomy of meaning is in general too fine to be adequate for word sense representation and disambiguation. Performance is understandably low (50-70%), given that word distinction is very fine-grained.
Gale, Church and Yarowsky (1992) reported a disambiguation method that uses bilingual corpus. Six words including "duty," "drug," "land," "language," "position," and "sentence" are studied. Their method relies on two translations in French for each word to tag training material for WSD. For instance, a sentence contains an instance of "duty" is tagged with the TAX sense, if the translation contains the French word "droit," and the OBLIGATION sense if the translation contains "devoir." The content words in the 100-word context of the ambiguous words are taken as contextual indicators of word sense. The average precision is 90% for two-way sense disambiguation. Dagan et al. (1994) also used translations in a bilingual lexicon to represent both word sense and contextual indicators. The authors show that the use of corpus in English is very effective in resolve sense ambiguity of sentences in Hebrew. Experimental results shows that 70% of the polysemous words are applicable for disambiguation. About 92% of these applicable polysemous words are disambiguated correctly. Yarowsky (1992) used the major categories in Roget's thesaurus as its sense tags. The statistical approach of the disambiguation method is the concordance set, the collection of the words in the 100-word context of a Roget category in Grolier's Encyclopedia. An average 92% accuracy rate was reported for 12 polysemous words for 3-way disambiguation on the average.
In most of the above-mentioned works, the sense division is either determined by hand or is taken directly from an existing source faulted with gaps. We have shown that the fine-grained senses in MRD can be used to fill gaps in the topical sets in LLOCE to arrive at a more complete and appropriate sense division for WSD. We experimented with the inclusion of conceptual relation for enhancement on coverage. Directly comparing methods is often difficult. Nevertheless, it is evident that in comparison our algorithms are simpler, take up less time and space overhead, and most importantly require no human intervention in all phases of WSD (sense division, tagging of training material, deduction of rules). This assumption seems to hold out, for experiments have shown that with a much smaller training corpus, the algorithms still provide broad-coverage sense disambiguation at comparable level of precision for text with many types of genres.

Conclusions and Future Work
The proposed method herein takes advantages of a number of linguistic phenomena: (1) Division of senses is primarily along the line of subject and topic. (2) Rather rigid schemes of text generation and predictable semantic relations are used to define senses in MRDs such as LDOCE. (3) The implicit links between instances of many of these relations are available in a thesaurus such as LLOCE. (4) Word senses seems to form semantic clusters that are effective knowledge sources for WSD.
This work also underscores the effectiveness of concept-based approach to WSD. Relation between concepts can be acquired efficiently from tagged bilingual corpora for broad and precise WSD.