Pruning the WordNet Model

The interrogation of the WordNet model has revealed many faults and inconsistencies in the relations (§2.2.2). While correction of all of these is highly desirable, the scope of such an operation is extremely broad and would require a great deal of manual lexicographic effort which would clearly not be possible within the project timeline. While correction of the WordNet sentence frames has been attempted, and this could be a

98 _{file Antonyms.csv (Appendix 27)} 99 _{file Top ontology.csv (Appendix 26)}

step towards the correction of the verb taxonomy (§§1.3.2.7, 2.3.2, 2.4), bringing this line of research to a satisfactory conclusion falls outside the scope of this project. Consequently, correction prior to morphological enrichment has been confined to the removal of disconnected proper nouns and limited rationalisation of relations where the process can be automated. The changes made are briefly discussed here in the order in which they are executed100. The phases involved are elimination of CLASS_MEMBER relations, replacement of adjectival SIMILAR-CLUSTERHEAD relations with HYPERNYM-HYPONYM relations, elimination of PERTAINYM relations between adjectives, a reduction of the number of disconnected proper nouns and the replacement of PERTAINYM and ANTONYM relations between word senses with the same type of relations between the corresponding synsets.

4.3.1 The CLASS_MEMBER Relation

The CLASS_MEMBER relation is used in WordNet to categorise how words are used as distinct from what they mean. It is the only relation type with subtypes: TOPICAL, REGIONAL and USAGE.

• TOPICAL class-membership relationships hold between noun synsets representing narrow categories and adjectives which apply to them, e. g. "chirpy" is a member of class "bird". The synset {"vegetation "; "flora"; "botany"} has TOPICAL members {"mown"; "cut"; " unmown"; "uncut"; "sprouted"; "dried- up"; "sere"; "sear"; "shriveled"; "shrivelled"; "withered"}.

• REGIONAL class-membership has been used to associate word senses with their countries of currency. Some British terms not used in America are associated with the synset representing Great Britain; much smaller sets are given for Scotland, Canada and the United States.

• The main USAGE classes are all categories of words and phrases, such as "plural", "disparagement", "ethnic slur", "slang", "trademark", "trade name" and

"colloquialism". "Ping-Pong" and "carborundum" are both encoded as trademarks. USAGE has also been used extensively in error for REGIONAL (e. g. "baking tray", "zebra crossing" and "sandpit" are encoded as USAGE members of the REGIONAL class representing Great Britain).

The sets of class members are incomplete, the range of classes is arbitrary and the encoding is erratic. It would be possible to add fields to the WordSense class to indicate its status with respect to each subtype, but there is not enough information provided to make this a worthwhile exercise. For these reasons, all CLASS_MEMBER relations and their converses have been deleted101.

4.3.2 SIMILAR and CLUSTERHEAD Relations

Adjectives in WordNet are organised in a completely different way from nouns and verbs, in that no HYPERNYM-HYPONYM relations are encoded. These are replaced by SIMILAR-CLUSTERHEAD relations, where an adjective clusterhead maps by a SIMILAR relation to several adjective satellites, but no adjective can be at one and the same time a clusterhead and a satellite. A sample was taken of 106 SIMILAR relations, which were then classified manually (Table 36).

In 70% of cases the clusterhead is the HYPERNYM of the satellite. Every SIMILAR relation has been replaced with a HYPONYM relation and every CLUSTERHEAD relation with a HYPERNYM relation102, for the following reasons:

• the level of accuracy (70%: Table 36) is as good as that found in the verb taxonomy (§2.2.2);

• having the same kind of taxonomy for adjectives as for nouns will facilitate the application of any WSD algorithm which uses HYPONYM and HYPERNYM relations (§6.1); 101 Secator.abolishClassMembership() 102 Secator.changeclusterHeadToHypernyms()

• because HYPERNYM/ HYPONYM relations have not been allowed between adjectives, PERTAINYM relations have been used, inconsistently, to link adjectives, (§4.3.3).

Table 36: Classification of SIMILAR-CLUSTERHEAD relations

Category Instances

Clusterhead is hypernym of satellite 74 Satellite is hypernym of clusterhead 8 Clusterhead is synonym of satellite 15 Clusterhead is sister of satellite 3 Clusterhead is unrelated to satellite 6

TOTAL 106

Table 37: Reclassification of PERTAINYM relations between adjectives

New Relation Instances SIMILAR 25 DERIV 12 ANTONYM 1 Total 38

4.3.3 Adjective to Adjective PERTAINYM Relations

The PERTAINYM relation is used typically to indicate the noun from which an adjective is derived or the adjective from which an adverb is derived, and clearly expresses a semantic and not merely a lexical relationship. In preparation for the re-encoding of these relations between synsets, representing meanings, instead of between word senses (§4.3.5), a few cases were unexpectedly discovered of PERTAINYM relations between two adjectives. The semantic import of these relations cannot be the same as in the other cases. Examination of the adjective to adjective PERTAINYMS103 (Appendix 28) showed that they could all be reclassified as SIMILAR, DERIV or ANTONYM. The number of instances of each reclassification is shown in Table 37. Reclassification as SIMILAR would violate the rule that an adjective must be a CLUSTERHEAD or a SATELLITE but not both (§4.3.2, Appendix 65). This was an additional reason for

replacing SIMILAR relations with HYPONYM relations (§4.3.2). Therefore the relations reclassified as SIMILAR in Appendix 28 have been re-encoded as HYPONYM104 and the remainder have been re-encoded as they were reclassified.

4.3.4 Proper Nouns

WordNet 3.0 contains many proper nouns, often connected to the rest of the graph only by CLASS-MEMBER, INSTANCE-INSTANTIATED or MERONYM-HOLONYM relations. CLASS-MEMBER relations have already been removed (§4.3.1); INSTANCE relations encode mainly proper names as instances (in the opinion of the encoders) of various concepts encapsulated by synsets, including such niceties as "Einstein was a genius", and provide incomplete lists for such categories as "physicist" and "king". The selection is narrow and intrinsically arbitrary. It is hard to see the reason for including this kind of encyclopaedic information in a lexical database; MERONYM-HOLONYM relations are used to identify the geographical locations of towns, rivers etc. This world

knowledge again belongs in an encyclopaedia rather than a lexical database. While there

may have been some justification for including this kind of information in the past, there is none since the advent of easily accessible encyclopaedic resources such as Wikipedia.

On the other hand, proper names such as names of countries may be relevant when they are linked to adjectives referring to nationality. It is useful to retain PERTAINYM relations such as between "French" and "France". Accordingly an algorithm105 was developed to delete those proper nouns which have only CLASS-MEMBER, INSTANCE-INSTANTIATED or MERONYM-HOLONYM relations.

104

Secator.abolishAdjectiveToAdjectivePertainyms

105

Secator.removeProperNouns was the first algorithm developed for the purpose of modifying the data content of the WordNet model. It required a method for synset deletion which gave rise to a consideration of how safely to delete synsets in this or any other circumstance. Synset deletion must ensure:

• that all relations targeted on the synset to be deleted are also deleted;

• that a concurrent modification error is avoided if iterating through the Synset map; that the lexicon is marked as inconsistent until it can be revised.