A verb is the organizational core of a sentence. Understanding the meaning of the verb is, therefore, a key to understanding the meaning of the sentence. One of the ways we can formulate natural language understanding is by treating it as a task of mapping natural language text to its meaning representation: entities and relations anchored to the world. Since verbs express relations over their arguments and adjuncts, a lexical resource about verbs can facilitate natural language understanding by mapping verbs to relations over entities expressed by their arguments and adjuncts in the world.
In this thesis, we semi-automatically construct a verb resource called VerbKB that contains important semantics for natural language understanding. A verb lexical unit in VerbKB consists of a verb lexeme or a verb lexeme and a preposition e.g., "live", "live in", which is typed with a pair of NELL semantic categories that indicates its subject type and its object type e.g., "live in"(person, location).
We present algorithms behind VerbKB that learn two semantic types of mappings for these verb lexical units that will complement existing resources of verbs such as WordNet and VerbNet and existing knowledge bases about entities such as NELL. The two semantic types of mappings are (1) the mappings from verb lexical units to binary relations in knowledge bases (e.g., the mapping from the verb lexical unit "die at"(person, nonNegInteger) to the binary relation personDiedAtAge) and (2) the mappings from verb lexical units to changes in binary relations in knowledge bases (e.g., the mapping from the verb lexical unit "divorce"(person, person) to the termination of the relation hasSpouse). The mappings from verb lexical units to binary relations in knowledge bases such as NELL, YAGO, or Freebase can provide a direct link between the text and the background knowledge about the world contained in these knowledge bases, enabling inferences over the world knowledge to better understand the text. The mappings from verb lexical units to changes in binary relations in knowledge bases can facilitate automatic updates of relations and temporal scoping of relations in the knowledge bases.