Putting the corpus into the dictionary

A corpus is an arbitrary sample of language, whereas a dictionary aims to be a systematic account of the lexicon of a language. Children learn language through encountering arbitrary samples, and using them to build systematic representations. These banal observations suggest a relationship between corpus and dictionary in which the former is a provisional and dispensable resource used to develop the latter. In this paper we use the idea to, first, review the Word Sense Disambiguation (WSD) research paradigm, and second, guide our current activity in the development of the Sketch Engine, a corpus query tool. We develop a model in which a database of mappings between collocations and meanings acts as an interface between corpus and dictionary. 1 Putting the dictionary in the corpus. Consider SEMCOR, a hugely successful project and resource, very widely used and stimulating large amounts of WSD work. It is clearly a dynamic and important model, only exceeded in its take-up and impact by the WordNet database itself. SEMCOR inserts dictionary sense labels into the corpus. It “puts the dictionary into the corpus”; like our title, but the other way round. Call this the PDIC model, as opposed to our preferred PCID model. If one thinks of WSD as a task on the verge of hitting the marketplace and being widely used in applications, then PDIC is appropriate, as it represents the WSD task successfully done, and can be used as a model for what a system should do. However it is widely acknowledged that WSD is not in any such near-to-market situation (as shown by discussions at the SENSEVAL-3 workshop 1 ) and 1 Notes available at http://www.senseval. org/senseval3 we stand by our deep reservations about the nature of the WSD task (Kilgarriff 1997a, 1997b) which imply this is unlikely to change. An alternative model, closer to the observations of the opening paragraph, is that the larger task is at a further remove from applications and is better seen as lexical acquisition. We are not yet at a stage (and probably never will be) at which a general–purpose WSD module is a relevant goal, but there are many language interpretation tasks which cannot be done without richer lexical representations. In the PCID model, the corpus serves to enrich the lexicon. 1.1 Levels of abstraction The direct approach to corpus-dictionary linkage is to put pointers to dictionary senses into the corpus (in the PDIC model, as in SEMCOR) or (in the PCID model) to put pointers to corpus instances of words into the dictionary. The direct approach has a number of drawbacks. The primary practical one is fragility. If the corpus (PCID model) or the dictionary (PDIC model) is edited or changed in any way, maintenance of the links is a headache. (This has been an ongoing issue for SEMCOR, as new versions of WordNet call for re-building SEMCOR in ways which cannot in general be fully and accurately automated; see Daudé et al (2000, 2001)). The theoretical one concerns levels of abstraction. A dictionary is an abstract representation of the language, in which we express differences of meaning but are not engaged with specifics of differences of form. The corpus is at the other end of the scale: the differences of form are immediately present but differences of meaning can only be inferred. What is needed is an intermediate level which links both to the meaningdifferences in the dictionary and to the specific instances in the corpus.ion. A dictionary is an abstract representation of the language, in which we express differences of meaning but are not engaged with specifics of differences of form. The corpus is at the other end of the scale: the differences of form are immediately present but differences of meaning can only be inferred. What is needed is an intermediate level which links both to the meaningdifferences in the dictionary and to the specific instances in the corpus. Our candidate for this role is the grammaticalrelation triple, comprising <grammatical-relation, word1, word2> (examples are <object, drink, beer> and <modifier, giant, friendly>). Triples such as these 2 have, of late, been very widely used in NLP, as focal objects for parsing and parse evaluation (eg Briscoe and Carroll 1998, Lin 1998), thesaurusbuilding (eg Grefenstette 1992) and for building multilingual resources from comparable corpora (eg Lű and Zhou 2004). Approaching from the other end, they are increasingly seen as core facts to be represented in dictionaries by lexicographers, who usually call them just collocations (COBUILD 1987, Oxford Collocations Dictionary 2003). In our own work, we compile sets of all the salient collocations for a word into ‘word sketches’, which then serve as a way of representing corpus evidence to the lexicographer (Kilgarriff and Tugwell 2001, Kilgarriff and Rundell 2002, Kilgarriff et al 2004). 1.2 Aside: Parsing and lemmatizing Few would dispute that collocations which incorporate grammatical information (and are thereby triples like <object, drink, beer> ) are a more satisfactory form of lexical object than ‘raw’ collocates – those words occurring within a window of 3 or 5 words to the right of, or to the left of, or on either side of the node word. Windowing approaches operate as a proxy for grammatical information and are appropriate only where there is no parser available, or it is too slow, or too often wrong. Historically, these factors have often applied and most older work uses windowing rather than grammar. As we are able to work with grammar, we do. We are repeatedly struck by how much cleaner results we get. We also find it preferable to work with lemmas rather than raw word forms, where a lemmatiser is available for a language. 1.3 Terminology “Grammatical relation triples” being unwieldy, I shall call these objects simply “collocations”, or say that the one word is the other’s “collocate”. Strictly, the items in the triples are lemmas which include a word class label (noun, verb, adj etc) as well as the base form; in examples, the word class labels will be omitted for readability. Naturally, some grammatical relations are duals (object, object-of) or symmetrical (and/or); for a full treatment see Kilgarriff and Tugwell (2001). 2 Naturally, details vary between authors. Briscoe and Carroll do not in fact use triples, but tuples with further slots for particles and further grammatical specification. 2 The collocation database In the proposed model, between the dictionary and the corpus sits a database. For each dictionary headword, there is a set of records in this database comprising 1) a collocate (including grammatical relation) 2) a pointer to the dictionary sense 3) a set of pointers to corpus instances of the headword in this collocation The database is, in the first instance, generated from the corpus, so the corpus links are immediately available. To start with, the dictionary pointers are not filled in for polysemous words. (For monosemous words, the links can immediately be inserted.) A word sketch (see Fig 1) is an example of such a database. The corpus links are present, implemented as hyperlinked URLs: for on-line readers, clicking on a number opens up a concordance window for the collocation (go to www.sketchengine.co.uk to self-register for an account). 2.1 Limitations and potential extensions The word sketch model is dependent on Yarowsky’s “One sense per collocation” (Yarowsky 1993). To the extent that this does not hold, the model will be inadequate and we will need to make the structure of the database record richer. The triples formalism does not readily express some kinds of information which are known to be relevant to WSD. An intermediate database to link dictionary to corpus should have a place for all relevant facts. Two kinds of fact which do not obviously fit the triples model are grammatical constructions, and domain preferences. Many, possibly all, grammatical constructions can be viewed as grammatical relations (with the “other word” field null). Thus a verb like found, when in the passive, means “set up” (“the company was founded in 1787”). In this case we associate the triple <passive, found, _> with the “set up” meaning. We have already implemented a range of “unary relations” within the Sketch Engine, and believe this approach will support the description of all grammatical constructions. As much recent work makes clear, domains are central to sense identification (eg Agirre et al 2001, Buitelaar and Sacaleanu 2001, McCarthy et al 2004). However it is far from clear how domain goal bnc freq = 10631 change options