A Model for Contextualizing Natural Language Discourse

This paper describes a computational model of semantic processing in natural language discourse understanding based on the distribution of knowledge over multiple spaces as proposed by Fauconnier (1985), Dinsmore (1987a), K a m p (1980), Johnson-Laird (1985) and others. Among the claims made about such a partitioned representation of knowledge are the following: First, it promotes a more direct, more natural mapping from surface discourse sentence to internal representation. Second, it supports more efficient reasoning and retrieval processes over that internal representation. Finally, it provides an accurate account of many of the most recalcitrant problems in natural language discourse understanding. Among these are implicit information, presupposition, referential opacity, tense and aspect, and common-sense reasoning in complex domains. The model identifies two fundamental levels of semantic processing: contextualization, in which an appropriate space for assimilating the information conveyed in a discourse sentence is located, and construction, in which the information is actually assimilated into that space. Contextualization allows the full semantics of the discourse to be realized implicitly in the internal representation. It also accounts for the use of moods, tenses, and various adverbials in discourse. The interaction of the contextualization processes with the semantics of aspectual operators provides an account of the discourse use of aspect

Specifying Logic Programs in Controlled Natural Language

Writing specifications for computer programs is not easy since one has to take into account the disparate conceptual worlds of the application domain and of software development. To bridge this conceptual gap we propose controlled natural language as a declarative and application-specific specification language. Controlled natural language is a subset of natural language that can be accurately and efficiently processed by a computer, but is expressive enough to allow natural usage by non-specialists. Specifications in controlled natural language are automatically translated into Prolog clauses, hence become formal and executable. The translation uses a definite clause grammar (DCG) enhanced by feature structures. Inter-text references of the specification, e.g. anaphora, are resolved with the help of discourse representation theory (DRT). The generated Prolog clauses are added to a knowledge base. We have implemented a prototypical specification system that successfully processes the specification of a simple automated teller machine.Comment: 16 pages, compressed, uuencoded Postscript, published in Proceedings CLNLP 95, COMPULOGNET/ELSNET/EAGLES Workshop on Computational Logic for Natural Language Processing, Edinburgh, April 3-5, 199

Attempto - From Specifications in Controlled Natural Language towards Executable Specifications

Deriving formal specifications from informal requirements is difficult since one has to take into account the disparate conceptual worlds of the application domain and of software development. To bridge the conceptual gap we propose controlled natural language as a textual view on formal specifications in logic. The specification language Attempto Controlled English (ACE) is a subset of natural language that can be accurately and efficiently processed by a computer, but is expressive enough to allow natural usage. The Attempto system translates specifications in ACE into discourse representation structures and into Prolog. The resulting knowledge base can be queried in ACE for verification, and it can be executed for simulation, prototyping and validation of the specification.Comment: 15 pages, compressed, uuencoded Postscript, to be presented at EMISA Workshop 'Naturlichsprachlicher Entwurf von Informationssystemen - Grundlagen, Methoden, Werkzeuge, Anwendungen', May 28-30, 1996, Ev. Akademie Tutzin

Attempto Controlled English (ACE)

Attempto Controlled English (ACE) allows domain specialists to interactively formulate requirements specifications in domain concepts. ACE can be accurately and efficiently processed by a computer, but is expressive enough to allow natural usage. The Attempto system translates specification texts in ACE into discourse representation structures and optionally into Prolog. Translated specification texts are incrementally added to a knowledge base. This knowledge base can be queried in ACE for verification, and it can be executed for simulation, prototyping and validation of the specification.Comment: 13 pages, compressed, uuencoded Postscript, to be presented at CLAW 96, The First International Workshop on Controlled Language Applications, Katholieke Universiteit Leuven, 26-27 March 199

Using Intentaional and Attentional Structure for Anaphor Resolution

This paper describes the Scenes knowledge representation that captures the intentional and attentional structure of discourse. Using this information a natural language interface can isolate context and resolve anaphors with focusing heuristics. Further, anaphor resolution can be coordinated with interruptions so that completed digressions are ignored

A Knowledge-based approach to understanding natural language

Understanding a natural language requires knowledge about that language as a system of representation. Further, when the task is one of understanding an extended discourse, world knowledge is also required. This thesis explores some of the issues involved in representing both kinds of knowledge, and also makes an effort to arrive at some under standing of the relationship between the two. A part of this exploration involves an examination of some natural language understanding systems which have attempted to deal with extended discourse both in the form of stories and in the form of dialogues. The systems exam ined are heavily dependent on world knowledge. Another part of this exploration is an effort to build a dialogue system based on speech acts and practical argu ments, as they are described in Recognizing Promises, Advice, Threats, and Warnings , a Masters Thesis presented to Rochester Institute of Technology, School of Computer Science and Technology, in 1986 by Kevin Donaghy. This dialogue system includes a deterministic syntactic parser, a semantic representation based on the idea of case frames, and a context interpreter that recognizes and represents groups of sentences as practical arguments. This Prolog implementation employs a frame package developed and described in A Frame Virtual Machine in C-Prolog , a Masters Thesis presented to Rochester Institute of Technology, School of Computer Science and Technology, in 1987 by LeMora Hiss. While this automated dialogue system is necessarily limited in the domain that it recognizes, the opportunity it allows to build a mechanism and a system of representation brings with it a range of issues from the syntactic, through the semantic, to the contextual and the pragmatic. Here, the focus of inquiry came to settle in the semantic representa tion, where the relationship between knowledge about language and knowledge about the world seems to be naturally resident

Natural Arabic language text understanding

The most challenging part of natural language understanding is the representation of meaning. The current representation techniques are not sufficient to resolve the ambiguities, especially when the meaning is to be used for interrogation at a later stage. Arabic language represents a challenging field for Natural Language Processing (NLP) because of its rich eloquence and free word order, but at the same time it is a good platform to capture understanding because of its rich computational, morphological and grammar rules. Among different representation techniques, Lexical Functional Grammar (LFG) theory is found to be best suited for this task because of its structural approach. LFG lays down a computational approach towards NLP, especially the constituent and the functional structures, and models the completeness of relationships among the contents of each structure internally, as well as among the structures externally. The introduction of Artificial Intelligence (AI) techniques, such as knowledge representation and inferencing, enhances the capture of meaning by utilising domain specific common sense knowledge embedded in the model of domain of discourse and the linguistic rules that have been captured from the Arabic language grammar. This work has achieved the following results: (i) It is the first attempt to apply the LFG formalism on a full Arabic declarative text that consists of more than one paragraph. (ii) It extends the semantic structure of the LFG theory by incorporating a representation based on the thematic-role frames theory. (iii) It extends to the LFG theory to represent domain specific common sense knowledge. (iv) It automates the production process of the functional and semantic structures. (v) It automates the production process of domain specific common sense knowledge structure, which enhances the understanding ability of the system and resolves most ambiguities in subsequent question-answer sessions

Reference Specification in Multilingual Document Production

To produce documents in multiple languages automatically requires a language-independent representation of the documents' meaning. For a person to build this language-independent representation by communicating in natural language with a computer system, the problem of reference must be addressed. This problem, inherent in natural language, presents itself not only in the specification of the language-independent representation, but also in the generation of documents with the meaning contained in this representation. This thesis presents methods to make both the specification of entities in the user interface and the generation of expressions to refer to these entities in documents more natural and provides empirical evidence demonstrating the efficacy of these methods. More specifically, this thesis describes the development of three types of reference mechanisms: a statistical model that uses domain and lexical knowledge to organize new options in the interface; techniques for controlling coreference specification that take advantage of discourse structure and genre features; and automatically learned models for generating expressions to refer to new and already mentioned entities in a particular domain. The evaluation of these reference mechanisms establishes that specifying new entities using an interface formed by computational linguistic processing reduces the amount of time required to refer to entities in the interface; exploiting discourse structure and genre features is more helpful than traditional knowledge-editing interfaces for referring to entities in the interface that are already contained in the knowledge representation; and using learned linguistic information to generate referring expressions in documents leads to expressions that more closely match the decisions of people.Engineering and Applied Science