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.

Since collocateswere not given explicitly in the original dictionarycontent of ODE, the task involves examining allavailable elements of a sense for clues which maypoint to collocational patterns. The most fruitful areas in this respect are firstlydefinition patterns, and secondly example sen-tences.

Definition patterns are best illustrated by verbs,where likely subjects and or objects are often indi-cated in parentheses:fly: of a bird, bat, or insect move through theair…impound: of a dam hold back water …The terms in parentheses can be collected as possi-ble collocates, and in some cases can be used asseeds for the generation of longer lists by exploit-ing the semantic classifications described in sec-tion 3 above.

Similar constructions are oftenfound in adjective definitions. For other parts ofspeech e. The de-fining style in ODE is regular enough to supportthis approach with some success. Example sentences can be useful sources sincethey were chosen principally for their typicality, The key problem is to identify automatically whichwords in the sentence represent collocates, as op-posed to those words which are merely incidental.

Syntactic patterns can help here; if looking for col-locates for a noun, for example, it makes sense tocollect any modifiers of the word in question, andany words participating in prepositional construc-tions. Thus if a sense of the entry for breach hasthe example sentenceShe was guilty of a breach of trust. However, it will be apparent from this that ex-amination of the content of a sense can do no morethan build up lists of candidate collocates — anumber of which will be genuinely high-scoringcollocates, but others of which may be more or lessarbitrary consequences of an editorial decision.

The second step will therefore be to build into theprocess a means of testing each candidate against acorpus-based list of collocates, in order to elimi-nate the arbitrary items and to extend the list thatremains7 ConclusionIn order for a non-formalized, natural-languagedictionary like ODE to become properly accessibleto computational processing, the dictionary contentmust be positioned within a formalism which ex-plicitly enumerates and classifies all the informa-tion that the dictionary content itself merelyassumes, implies, or refers to.

Such a system canthen serve as a means of entry to the original dic-tionary content, enabling a software application toquickly and reliably locate relevant material, andguiding interpretation. The process of automatically generating such aformalism by examining the original dictionarycontent requires a great deal of manual supervisionand ad hoc correction at all stages. Nevertheless,the process demonstrates the richness of a largenatural-language dictionary in providing cues andflagging exceptions.

The stylistic regularity of adictionary like ODE supports the enumeration of afinite albeit large list of structures and patternswhich can be matched against a given entry or en-try element in order to classify it, mine it for perti-nent information, and note instances which may beanomalous.

The formal lexical data is being built up along-side the original dictionary content in a single inte-grated database. This arrangement supports a broadrange of possible uses. Tanglish to Tamil converter, FREE tool to transliterate english to tamil online, write Tamil comments online using this Tamil translate tool. This is a list of English words that are borrowed directly or ultimately from Dravidian languages.

Dravidian languages include Tamil, Malayalam, Kannada, Telugu, and a number of other languages spoken mainly in South Asia. The list is by no means exhaustive. Some of the words can be traced to specific languages, but others have disputed or uncertain origins. Hindi Tamil Dictionary. Developer: Bede Products. Price: Free. Invaluable products at an affordable price. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

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