Hence its coverage is very extensive,and definitional detail is very rich. By the sametoken, however, a certain level of knowledge isassumed on the part of the reader, so not every-thing is spelled out explicitly. For example, ODEfrequently omits morphology and variation whichis either regular or inferable from related words.
Entry structure and defining style, while mostlyconforming broadly to a small set of basic patternsand formulae, may often be more concerned withdetail and accuracy than with simplicity of expla-nation. Such features make the ODE content rela-tively difficult to convert into comprehensive andformalized data.
Nevertheless, the richness of theODE text, particularly in the frequent use of exam-ple sentences, provides a wealth of cues and clueswhich can help to control the generation of moreformal lexical data. A basic principle of this work is that the en-hanced data should always be predicated on theoriginal dictionary content, and not the other wayround. There has been no attempt to alter the origi-nal content in order to facilitate the generation offormal data.
The enhanced data is intended primar-ily to constitute a formalism which closely reflects,summarizes, or extrapolates from the existing dic-tionary content.
The following sections list some of the data typesthat are currently in progress:2 MorphologyA fundamental building block for formal lexicaldata is the creation of a complete morphologicalformalism verb inflections, noun plurals, etc. This is being donelargely automatically, assuming regular patterns asa default but collecting and acting on anything inthe entry which may indicate exceptions explicitgrammatical information, example sentences,pointers to other entries, etc.
The original intention was to generate a morpho-logical formalism which reflected whatever wasstated or implied by the original dictionary content. Hence pre-existing morphological lexicons werenot used except when an ambiguous case needed tobe resolved.
As far as possible, issues relating tothe morphology of a word were to be handled bycollecting evidence internal to its dictionary entry. However, it became apparent that there weresome key areas where this approach would fallshort.
For example, there are often no conclusiveindicators as to whether or not a noun may be plural. In such cases, anyavailable clues are collected from the entry but arethen weighted by testing possible forms against acorpus.
Variation and alternativewording is embedded parenthetically in the lemma: as nice or sweet as pieObjects, pronouns, etc. Initially, a relatively small number of senses wereclassified manually. Statistical data was then gen-erated by examining the definitions of these senses.
Applied iteratively,this process succeeded in classifying all nounsenses in a relatively coarse-grained way, and isnow being used to further refine the granularity ofthe taxonomy and to resolve anomalies. This is the most significantnoun in the definition — not a rigorously definedconcept, but one which has proved pragmaticallyeffective.
The second element is a scoring of all the othermeaningful vocabulary in the definition i. A simple weight-ing scheme is used to give slightly moreimportance to words at the beginning of a defini-tion e.
These two elements are then assigned mutual in-formation scores in relation to each possible classi-fication, and the two MI scores are combined inorder to give an overall score. This enables one very readily to rank and group allthe senses for a given classification, thus exposingmisclassifications or points where a classificationneeds to be broken down into subcategories. The dictionary con-tains 95, defmed noun senses in total, so thereare on average 76 senses per node. However, thisaverage disguises the fact that there are a smallnumber of nodes which classify significantly largersets of senses.
Further subcategorization of largesets is desirable in principle, but is not considered apriority in all cases. For example, there are severalhundred senses classified simply as tree; the effortinvolved in subcategorizing these into various treespecies is unlikely to pay dividends in terms ofvalue for normal NLP applications.
At this level, auto It should be noted that a significant number ofnouns and noun senses in ODE do not have defini-tions and are therefore opaque to such processes. Firstly, some senses cross-refer to other defini-tions; secondly, derivatives are treated in ODE asundefined subentries. Classification of these willbe deferred until classification of all defmed sensesis complete.
It is anticipated that thiswill support the extraction of specialist lexicons,and will allow the ODE database to function as aresource for document classification and similarapplications. As with semantic classification above, a numberof domain indicators were assigned manually, andthese were then used iteratively to seed assignmentof further indicators to statistically similar defini-tions. Automatic assignment is a little morestraightforward and robust here, since most of thetime the occurrence of strongly-typed vocabularywill be a sufficient cue, and there is little reason toidentify a key term or otherwise parse the defini-tion.
Similarly, assignment to undefined items e. For longer entries this process has to bechecked manually, since the derivative may notrelate to all the senses of the parent. Currently, about 72, of a total ,senses and lemmas have been assigned domainindicators. There is no clearly-defined cut-off pointfor iterations of the automatic assignment process;each iteration will continue to capture senseswhich are less and less strongly related to the do-main. Beyond a certain point, the relationship willbecome too tenuous to be of much use in most con-texts; but that point will differ for each subjectfield and for each context.
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This dictionary covers the words you need for everyday use, and is useful for puzzle and crossword-solving. It is ideal for reference at home, at school, or in the office. With tens of thousands of regionally specific entries The Oxford Dictionary of English reflects the unique richness and global adaptability of English.
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