Knowledge Representation of Entity Attribute Frame for Natural Language Understanding

A Lexical Knowledge Representation Model for Natural Language Understanding

Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques. However, it is a daunting problem how to capture deep semantic information effectively and support the construction of a large-scale knowledge base efficiently. This paper describes a new knowledge representation mod...

Natural Language and Knowledge Representation

Natural Language Understanding with Knowledge

This paper examines the problem of extracting structured knowledge from unstructured free text. The extraction process is modeled after construction grammars, essentially providing a means of putting together form and meaning. The knowledge base is not simply treated as a destination, but also an important partner in the extraction process. In particular, the ideas are implemented as a module c...

Knowledge-Aware Natural Language Understanding

Natural Language Understanding (NLU) systems need to encode human generated text (or speech) and reason over it at a deep semantic level. Any NLU system typically involves two main components: The first is an encoder, which composes words (or other basic linguistic units) within the input utterances to compute encoded representations, that are then used as features in the second component, a pr...

Enriching Knowledge Sources for Natural Language Understanding

This paper presents the complete and consistent ontological annotation of the nominal part of WordNet. The annotation has been carried out using the semantic features defined in the EuroWordNet Top Concept Ontology and made available to the NLP community. Up to now only an initial core set of 1,024 synsets, the so-called Base Concepts, was ontologized in such a way. The work has been achieved b...