A Lexical Knowledge Representation Model 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...

Lexical Knowledge Representation and Natural Language Processing

Pustejovsky, J. and B. Boguraev, Lexical knowledge representation and natural language processing, Artificial Intelligence 63 (1993) 193-223. Traditionally, semantic information in computational lexicons is limited to notions such as selectional restrictions or domain-specific constraints, encoded in a "static" representation. This information is typically used in natural language processing by...

DATR: A Language for Lexical Knowledge Representation

Irregular lexemes are standardly regular in some respect. Most are just like regular lexemes except that they deviate in one or two characteristics. What is needed is a natural way of saying \this lexeme is regular except for this property". One obvious approach is to use nonmonotonicity and inheritance machinery to capture such lexical irregularity (and subregularity), and much recent research...

NKRL, a Knowledge Representation Language for Narrative Natural Language Processing

NKRL is a conceptual language which intends to provide a normalised, pragmatic description of the semantic contents (in short, the "meaning") of NL narrative documents. We introduce firstly the general architecture of NKRL, and we give some examples of its characteristic features. We supply, afterward, some sketchy information about the inference techniques and the NLP procedures associated wit...

Natural Language and Knowledge Representation