W ord N et Troponymy and Extraction of “Manner-Result” Relations (2018)

Abstract: Commonsense knowledge bases need to have relations that allow to predict the consequences of specific actions (say, if John stabbed Peter, Peter might be killed) and to unfold the possible actions for the specific results (Peter was killed. It could happen because of poisoning, stabbing, shooting, etc.) This kind of causal relations are established between manner verbs and result verbs: manner-result relations. We offer a procedure on how to extract manner-result relations from WordNet through the analysis of the troponym glosses. The procedure of extraction includes three steps and the results are based on the analysis of the whole set of verbs in WordNet.

## introduction wordnet (wn) as a database is widely used in variety of tasks related with extraction of semantic relations. verbs in wn are organized hierarchically as troponym-hypernym relations. meanwhile, the definition of troponym has something in common with the definition of a manner verb suggested by b. @xcite we consider in more details both types of relations: troponym-hypernym and manner verbresult verb relations. ## general procedure to extract manner-result relations from wordnet manner-result relations are hidden in the wn verb hierarchy. we know for sure that this kind of relations is a subset of troponym-hypernym relations. however, there are not any explicit ways to extract them yet. our idea is that manner-result relations can be extracted from the set of troponym-hypernym relations if two conditions, applied to troponymhypernym relation are valid: for example, if we consider the result synset {clean, make clean} as a hypernym, some its troponyms have glosses that satisfy the patterns: in this case, it can be stated that sweep, brush, steam, steam clean are manner verbs for clean and the following causality can be constructed: this idea is the basis of the general procedure for manner-result extraction. it includes 3 steps. ## scope of the results to evaluate what is the scope of the results we compare them with another type of causal relations that is explicitly presented in wn 3.1: cause-relation. cause-relation refers to the relation between two verbs v 1 and v 2 where v 1 logically causes v 2 @xcite . for example, the verb kill causes the verb die. running through 13789 verb synsets in wn 3.1 we automatically extracted 219 verb synsets that contain cause-relation. among them there are 63 verb synsets that cause the same synset. in other words, there are 63 causal relations with absolutely identical left and right sides: {dry, dry_out} causes {dry, dry_out} {lengthen} causes {lengthen}, etc. it happened because of polysemy in verb meaning. synsets here are formally identical but represent different meanings of verbs. since it is hard to use such kind of causality in applications, the real number of the verb synsets that contain cause-relation can be reduced to 156. comparison of 156 verb synsets containing logical cause-relation with 1541 non-logical (empirical) causal relations shows that the scope of the latter relations is significant. ## conclusions and future work in this paper, we have described how to extract manner verb-result verb causal relations from wn. the procedure of extraction includes 3 steps: a) extraction of the top verb synsets (total 564), b) extraction of the result synsets and the change-of-state synsets among them manually (total 127), c) running automatically the algorithm "v + by" and "v + with" on 127 top synsets and getting 1541 types of manner-result causal relations. the results are considered as preliminary ones. as future work, the algorithm can be elaborated by adding new patterns and tuning the original ones. for example, the change-of-state verb die has a troponym synset suffocate, stifle, asphyxiate (be asphyxiated; die from lack of oxygen) which clearly indicates the manner of dying but the gloss doesn't contain the patterns we are working with. these types of extracted relations can be widely used in commonsense knowledge bases for the prediction of action consequences and unfolding the possible reasons for the results. commonsense knowledge bases enriched by using this approach can be exploited in dialog systems and the other specific technologies and applications.