Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural language text. Their symbolic component is amenable to inspection by humans, while...
Existing grammar frameworks do not work out particularly well for controlled natural languages (CNL), especially if they are to be used in predictive editors. I introduce in this p...
This paper addresses the problem of learning to map sentences to logical form, given training data consisting of natural language sentences paired with logical representations of ...
Tom Kwiatkowksi, Luke S. Zettlemoyer, Sharon Goldw...
From the perspective of the linguist, the theory of formal languages serves as an abstract model to address issues such as complexity, learnability, information content, etc. which...
The volume of information in natural languages in electronic format is increasing exponentially. The demographics of users of information management systems are becoming increasin...
Controlled natural languages (CNL) and computational semantics in general do not address word sense disambiguation, i.e., they tend to interpret only some functional words that are...
We present the advantages of guided sentences composition for communicating in natural language with computers. We show how guidance can be achieved by means of the partial synthe...
We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor pr...
Objectives: To discuss the relationships between ontologies, terminologies and language in the context of Natural Language Processing (NLP) applications in order to show the negat...
Robert H. Baud, Werner Ceusters, Patrick Ruch, Ann...
In this paper an efficient architecture for natural language processing is presented, implemented in hardware using FPGAs (Field Programmable Gate Arrays). The system can receive s...
Christos Pavlatos, Alexandros C. Dimopoulos, Georg...