A foundation for semantic interpretation

Artificial intelligence, 1988

A new approach to semantic interpretation in natural language understanding is described, together with mechanisms for both lexical and structural disambiguation that work in concert with the semantic interpreter. ABSITY, the system described, is a Montague-inspired semantic interpreter. Like Montague formalisms, its semantics is compositional by design and is strongly typed, with semantic rules in one-to-one correspondence with the meaning-affecting rules of a Marcus parser. The Montague semantic objects--functors and truth conditions--are replaced with elements of the frame language FRAIL. ABSITY's partial results are always well-formed FRAIL objects.

An introduction to knowledge representation using Frame Semantics, as is being carried out in the FrameNet Project. A short news article is analyzed, providing examples of many of the questions being dealt with and the proposed solutions, including semantic composition, text coherence, polysemy and WSD, and evidentiality.

1993

2004

ACKNOWLEDGEMENTS I would like to acknowledge all those who helped make this thesis a reality. In particular, I would like to acknowledge Dr. Rada Mihalcea for her invaluable advice, support and guidance, which are very important to the thesis. I would like to acknowledge Dr. Paul Tarau and other members in NLP group for sharing their knowledge and interesting ideas in various meetings and conversations, Dr. Kathy Swigger for teaching me “Natural Language Processing” and “Artificial Intelligence”.

We present an approach to semantic interpretation of syntactically parsed Japanese sentences that works largely parser-independent. The approach relies on a standardized parse tree format that restricts the number of syntactic con gurations that the semantic interpretation rules have to anticipate. All parse trees are converted to this format prior to semantic interpretation. This setup allows us not only to apply the same set of semantic interpretation rules to output from di erent parsers, but also to independently develop parsers and semantic interpretation rules.

In Data-Oriented Parsing (DOP), an annotated language corpus is used as a stochastic grammar. The most probable analysis of a new input sentence is constructed by combining sub-analyses from the corpus in the most probable way. This approach has been succesfully used for syntactic analysis, using corpora with syntactic annotations such as the Penn Treebank. If a corpus with semantically annotated sentences is used, the same approach can also generate the most probable semantic interpretation of an input sentence. The present paper explains this semantic interpretation method, and summarizes the results of a preliminary experiment. Semantic annotations were added to the syntactic annotations of most of the sentences of the ATIS corpus. A data-oriented semantic interpretation algorithm was succesfully tested on this semantically enriched corpus.

This paper retraces the development of Database Semantics (DBS) from its beginnings in Montague grammar. It describes the changes over the course of four decades and explains why they were seen to be necessary. DBS was designed to answer the central theoretical question for building a talking robot: \vspace{0.1cm} {How does the mechanism of natural language communication work?} \vspace{0.1cm} \noindent For doing what is requested and reporting what is going on, a talking robot requires not only language but also non-language cognition. The contents of non-language cognition are re-used as the meanings of the language surfaces. Robot-externally, DBS handles the language-based transfer of content by using nothing but modality-dependent unanalyzed external surfaces such as sound shapes or dots on paper, produced in the speak mode and recognized in the hear mode. Robot-internally, DBS reconstructs cognition by integrating linguistic notions like functor-argument and coordination, philosophical notions like concept-, pointer-, and baptism-based reference, and notions of computer science like input-output, interface, data structure, algorithm, database schema, and functional flow.

International Journal of Man-Machine Studies, 1990

In this paper we describe a database interface that is loosely based upon some of the concepts proposed by Richard Montague in his approach to the interpretation of natural language. The system is implemented as an executable attribute grammar specified in a higher order, lazy, pure functional programming language. The attribute grammar formalism provides a simple means of implementing Montague's notion of "semantic rule to syntactic rule correspondence" and the higher order functional language in which the attribute grammar is constructed provides an appropriate vehicle for implementing Montague's higher order semantics. The purpose of the paper is twofold: (i) to demonstrate that many of Montague's ideas can be used to advantage in creating natural language interfaces to databases, and (ii) to introduce a method for implementing attribute grammars in functional languages that is suitable for investigating both grammars and semantic theories of language.

2018

This paper contributes a formalization of frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. It finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two featurebased, discriminative probabilistic (log-linear) models, one with latent variables to permit disambiguation of new predicate words. The parser is demonstrated to significantly outperform previously published results.

Proceedings of the 12th conference on Computational linguistics -, 1988