On the Identification of Temporal Clauses

Abstract

Abstract. This paper describes a machine learning approach to the identification of temporal clauses by disambiguating the subordinating conjunctions used to introduce them. Temporal clauses are regularly marked by subordinators, many of which are ambiguous, being able to introduce clauses of different semantic roles. The paper also describes our work on generating an annotated corpus of sentences embedding clauses introduced by ambiguous subordinators that might have temporal value. Each such clause is annotated as temporal or non-temporal by testing whether it answers the questions when, how often or how long with respect to the action of its superordinate clause. Using this corpus, we then train and evaluate personalised classifiers for each ambiguous subordinator, in order to set apart temporal usages. Several classifiers are evaluated, and the best performing ones achieve an average accuracy of 89.23% across the set of ambiguous connectives.

Knowledge accumulation through automatic merging of ontologies

“…A relevant work is (Puscasu et al, 2006). 13 As OM accumulates more knowledge, it will be more resistant to inconsistency creeping into its knowledge, since it will know more.…”

Cause and Condition in Time Clauses

“…Thus, English grammarians Quirk et al (1985), Huddleston et al (2003), Puscasu et al (2006) give a number of situations in which time clauses, in addition to temporal meaning, may introduce different meanings such as: cause, result, condition, concession, comparison, contrast, etc. Let's see these situations illustrated below:…”