We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over 15 years, uses a specially constructed NLP-oriented ontology and an ontological-semantic lexicon to translate English text into a custom ontology- motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an experimental Web service that monitors RSS news sources, processes the summaries of the news stories, and publishes a structured representation of the meaning of the text in the news story.

This paper describes one approach to document authoring and natural language generation being pursued by the Summer Institute of Linguistics in cooperation with the University of Maryland, Baltimore County. We will describe the tools provided for document authoring, including a glimpse at the underlying controlled language and the semantic representation of the textual meaning. We will also introduce The Bible Translator’s Assistant© (TBTA), which is used to elicit and enter target language data as well as perform the actual text generation process. We conclude with a discussion of the usefulness of this paradigm from a Bible translation perspective and suggest several ways in which this work will benefit the field of computational linguistics.

The topic of mood and modality (MOD) is a difficult aspect of language description because, among other reasons, the inventory of modal meanings is not stable across languages, moods do not map neatly from one language to another, modality may be realised morphologically or by free-standing words, and modality interacts in complex ways with other modules of the grammar, like tense and aspect. Describing MOD is especially difficult if one attempts to develop a unified approach that not only provides cross-linguistic coverage, but is also useful in practical natural language processing systems. This article discusses an approach to MOD that was developed for and implemented in the Boas Knowledge-Elicitation (KE) system. Boas elicits knowledge about any language, L, from an informant who need not be a trained linguist. That knowledge then serves as the static resources for an L-to-English translation system. The KE methodology used throughout Boas is driven by a resident inventory of parameters, value sets, and means of their realisation for a wide range of language phenomena. MOD is one of those parameters, whose values are the inventory of attested and not yet attested moods (e.g. indicative, conditional, imperative), and whose realisations include flective morphology, agglutinating morphology, isolating morphology, words, phrases and constructions. Developing the MOD elicitation procedures for Boas amounted to wedding the extensive theoretical and descriptive research on MOD with practical approaches to guiding an untrained informant through this non-trivial task. We believe that our experience in building the MOD module of Boas offers insights not only into cross-linguistic aspects of MOD that have not previously been detailed in the natural language processing literature, but also into KE methodologies that could be applied more broadly.

This article discusses the various aspects of designing a system for eliciting knowledge about language from informants. For each design aspect, various options for implementation are presented, along with their pros, cons, and repercussions for other parts of the knowledge elicitation system. A running example throughout the text is taken from the paradigmatic morphology elicitation module of a system called Boas, which elicits knowledge to support a machine translation system. The main point of the article is an argument about the necessity to analyze the design choice space for complex natural language processing (NLP) systems early, comprehensively, and overtly.

Ontological semantics is a theory of meaning in natural language and an approach to natural language processing (NLP) which uses an ontology as the central resource for extracting and representing meaning of natural language texts, reasoning about knowledge derived from texts as well as generating natural language texts based on representations of their meaning. Ontological semantics directly supports such applications as machine translation of natural languages, information extraction, text summarization, question answering, advice giving, collaborative work of networks of human and software agents, etc. Ontological semantics pays serious attention to its theoretical foundations by explicating its premises; therefore, formal ontology and its relations with ontological semantics are important. Besides a general brief discussion of these relations, the paper focuses on the important theoretical and practical issue of the distinction between ontology and natural language. It is argued that this crucial distinction lies not in the (inaccurately) presumed nonambiguity of the one and the well-established ambiguity of the other but rather in the constructed and overtly defined nature of ontological concepts and labels on which no human background knowledge can operate unintentionally to introduce ambiguity, as opposed to pervasive uncontrolled and uncontrollable ambiguity in natural language. The emphasis on this distinction, we argue, will provide better theoretical support for the central tenets of formal ontology by freeing it from the Wittgensteinian and Rortyan retreats from the analytical paradigm; it also reinforces the methodology of NLP by maintaining a productive demarcation between the language-independent nature of ontology and language-specific nature of the lexicons, a demarcation that has paid off well in consecutive implementations of ontological semantics and their applications in practical computer systems.

The paper introduces and advocates an ontological semantic approach to information security. Both the approach and its resources, the ontology and lexicons, are borrowed from the field of natural language processing and adjusted to the needs of the new domain. The approach pursues the ultimate dual goals of inclusion of natural language data sources as an integral part of the overall data sources in information security applications, and formal specification of the information security community know-how for the support of routine and time-efficient measures to prevent and counteract computer attacks. As the first order of the day, the approach is seen by the information security community as a powerful means to organize and unify the terminology and nomenclature of the field.

The modern computational lexical semantics reached a point in its development when it has become useful to compare the goals and methods of the various approaches to it. This article proposes several choices in terms of which these goals and methods can be discussed. It is argued that the central questions include the use of lexical rules for generating word senses; the role of syntax and formal semantics in the specification of lexical meaning; the use of a world model, or ontology, as the organizing principle for lexical‐semantic descriptions; the relation between static and dynamic resources; the commitment to descriptive coverage; the tradeoff between generalization and idiosyncracy; and finally, the adherence to the “supply side” (method‐oriented) or “demand side” (task‐oriented) ideology of research. The discussion is inspired by, but not limited to, the comparison between the generative lexicon approach and the ontologic semantic approach to lexical semantics.

This paper presents a semiautomatic technique for developing broad-coverage finite-state morphological analyzers for use in natural language processing applications. It consists of three components—elicitation of linguistic information from humans, a machine learning bootstrapping scheme, and a testing environment. The three components are applied iteratively until a threshold of output quality is attained. The initial application of this technique is for the morphology of low-density languages in the context of the Expedition project at NMSU Computing Research Laboratory. This elicit-build-test technique compiles lexical and inØectional information elicited from a human into a finite-state transducer lexicon and combines this with a sequence of morphographemic rewrite rules that is induced using transformation-based learning from the elicited examples. The resulting morphological analyzer is then tested against a test set, and any corrections are fed back into the learning procedure, which then builds an improved analyzer.

This paper is in a form unconventional in modern journals but traditional for the discussion of foundational questions: a dialogue. It is a form that makes it possible to contrast two deeply held but incompatible views, each with its standard forms of defence, in order to seek common ground and make the differences more precise. In artificial intelligence, or at least in the major part of it still committed to symbolic representations, there is a long history of discussion of the origin and nature of the symbols we use in representations, symbols which normally look like words, English words in fact, but which most researchers deny are such words, since to concede that would put in question the abstract nature of the representation. In what follows, we examine our common ground and then diverge over five specific questions on the issue of representations. The discussion focuses on symbol use in representations of language, because there the similarity is most acute—between the representation and the represented—but the issues are general and apply to symbolic AI as such.

This research paper explores a promising interface between natural language processing (NLP) and information assurance and security (IAS). More specificall~ it is devoted to possible applications to, and further dedicated development of, the accumulated considerable resources in NLP for, IAS. The expected and partially accomplished result is in harnessing the weird, illogical ways natural languages encode meaning, the very ways that defy all the usual combinatorial approaches to mathematical--and computational--complexity and make NLP so hard, to enhance information security. The paper is of a mixed theoretical and empirical nature. Of the four possible venues of applications, (i) memorizing randomly generated passwords with the help of automatically generated funny jingles, (ii) natural language watermarking, (iii) using the available machine translation (MT) systems for (additional) encryption of text messages, and (iv) downgrading, or sanitizing classified information in networks, two venues, (i) and (iv), have been at least partially implemented and the remaining two (ii) and (iii) are being implemented to the proof-of-concept level. We must make it very clear, however, that we have done very little experimentation or evaluation at this point, though we are moving quickly in that direction. The merits of the paper, if any, are in its venture to make considerable progress achieved recently in NLE especially in knowledge representation and meaning analysis, useful for IAS needs. The NLP approach adopted here, ontological semantics, has been developed by two of the coauthors; watermarking is based on the pioneering research by another coauthor and his associates; most of the implementation of the password memorization software has been done by the fourth coauthor. All the four of us have agonized whether we should report this research now or wait till we have fully implemented all or at least some of the systems we are developing. At the end of the day, we have reached a consensus that it is important, even at this early stage, to review for the information security community what NLP can do for it and to invite feedback and further efforts and ideas on what seems likely to become a new paradigm in information security. To the body of the paper, we Mikhail J. Atallah, Craig J. McDonough, Victor Raskin Center for Education and Research in Information Assurance and Security (CERIAS, www.cerias.purdue.edu) Purdue University W. Lafayette, IN 47907 mja, raskin, [email protected] Sergei Nirenburg Computing Research Laboratory, New Mexico State University Las Cruces, NM 88003 [email protected] have added two self-contained deliberately reference-free appendices on NLP and ontological semantics, respectively, primarily for the benefit of those IAS readers, who are interested in expanding their understanding of those fields and further exploring their possible fruitful interactions with IAS.

In this paper we describe a lexical disambiguation algorithm based on a statistical language model we call maximum likelihood disambiguation. The maximum likelihood method depends solely on the target language. The model was trained on a corpus of American English newspaper texts. Its performance was tested using output from a transfer based translation system between Turkish and English. The method is source language independent, and can be used for systems translating from any language into English.

The paper describes an approach to developing an interactive MT system for translating technical texts on the example of translating patent claims between Russian and English. The approach conforms to the human-aided machine translation paradigm. The system is meant for a source language (SL) speaker who does not know the target language (TL). It consists of i) an analysis module which includes a submodule of interactive syntactic analysis of SL text and a submodule of fully automated morphological analysis, ii) an automatic module for transferring the lexical and partially syntactic content of SL text into a similar content of the TL text and iii) a fully automated TL text generation module which relies on knowledge about the legal format of TL patent claims. An interactive analysis module guides the user through a sequence of SL analysis procedures, as a result of which the system produces a set of internal knowledge structures which serve as input to the TL text generation. Both analysis and generation rely heavily on the analysis of the sublanguage of patent claims. The model has been developed for English and Russian as both SLs and TLs but is readily extensible to other languages.

This paper deals with the discovery, representation, and use of lexical rules (LRs) during large-scale semi-automatic computational lexicon acquisition. The analysis is based on a set of LRs implemented and tested on the basis of Spanish and English business- and finance-related corpora. We show that, though the use of LRs is justified, they do not come cost-free. Semi-automatic output checking is required, even with blocking and preemtion procedures built in. Nevertheless, large-scope LRs are justified because they facilitate the unavoidable process of large-scale semi-automatic lexical acquisition. We also argue that the place of LRs in the computational process is a complex issue.

Patent claims are the subject of legal protection.They must be formulated according to a set of precise syntactic, lexical and stylistic guidelines.Composing patent claims is a complex task, even for experts.In this paper we report about an tmplemented system for supporting authoring claims for patents describing apparatuses.The system generates claim texts from the input specified partly by the stored conceptual text schemata and partly by the input from the user.The result of the interactive content acquisition stage is a shaUow-level representation which can be considered a draft to be automatically revised into the final text of the claim.

The findings of a workshop, the goals of which were to identify applications, research problems, and designs of high performance computing and communications (HPCC) systems for supporting applications are discussed. In computer vision, the main scientific issues are machine learning, surface reconstruction, inverse optics and integration, model acquisition, and perception and action. In speech and natural language processing (SNLP), issues were identified statistical analysis in corpus-based speech and language understanding, search strategies for language analysis, auditory and vocal-tract modeling, integration of multiple levels of speech and language analyses, and connectionist systems. In AI, important issues that need immediate attention include the development of efficient machine learning and heuristic search methods that can adapt to different architectural configurations, and the design and construction of scalable and verifiable knowledge bases, active memories, and artificial neural networks.>

A specialized transition network mechanism, the interruptable transition network (ITN) is used to perform the last of three stages in a multiprocessor syntactic parser. This approach can be seen as an exercise in implementing a parsing procedure of the active chart parser family.

Data models used in database management have not been built with AI applications in mind. The entities and their relationships in an AI environment transcend in complexity the data semantics of most other databases, so that the expressive power of the "usual" data models becomes insufficient. In AI community databases are viewed as a possible application area ("database front ends") but in AI research itself the databases used tend to be ad hoc and are not specified in terms of data models and DBMS based on such. The data model suggested below is a step towards bridging the gap between database theory and AI databases.