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arXiv1995

GRAMPAL: A Morphological Processor for Spanish implemented in Prolog

A model for the full treatment of Spanish inflection for verbs, nouns and adjectives is presented. This model is based on feature unification and it relies upon a lexicon of allomorphs both for stems and morphemes. Word forms are built by the concatenation of allomorphs by means of special contextual features. We make use of standard Definite Clause Grammars (DCG) included in most Prolog implementations, instead of the typical finite-state approach. This allows us to take advantage of the declarativity and bidirectionality of Logic Programming for NLP. The most salient feature of this approach is simplicity: A really straightforward rule and lexical components. We have developed a very simple model for complex phenomena. Declarativity, bidirectionality, consistency and completeness of the model are discussed: all and only correct word forms are analysed or generated, even alternative ones and gaps in paradigms are preserved. A Prolog implementation has been developed for both analysis and generation of Spanish word forms. It consists of only six DCG rules, because our {\em lexicalist\/} approach --i.e. most information is in the dictionary. Although it is quite efficient, the current implementation could be improved for analysis by using the non logical features of Prolog, especially in word segmentation and dictionary access.

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arXiv1995

An Efficient Algorithm for Surface Generation

A method is given that "inverts" a logic grammar and displays it from the point of view of the logical form, rather than from that of the word string. LR-compiling techniques are used to allow a recursive-descent generation algorithm to perform "functor merging" much in the same way as an LR parser performs prefix merging. This is an improvement on the semantic-head-driven generator that results in a much smaller search space. The amount of semantic lookahead can be varied, and appropriate tradeoff points between table size and resulting nondeterminism can be found automatically.

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arXiv1995

A Constraint-based Case Frame Lexicon Architecture

In Turkish, (and possibly in many other languages) verbs often convey several meanings (some totally unrelated) when they are used with subjects, objects, oblique objects, adverbial adjuncts, with certain lexical, morphological, and semantic features, and co-occurrence restrictions. In addition to the usual sense variations due to selectional restrictions on verbal arguments, in most cases, the meaning conveyed by a case frame is idiomatic and not compositional, with subtle constraints. In this paper, we present an approach to building a constraint-based case frame lexicon for use in natural language processing in Turkish, whose prototype we have implemented under the TFS system developed at Univ. of Stuttgart. A number of observations that we have made on Turkish have indicated that we need something beyond the traditional transitive and intransitive distinction, and utilize a framework where verb valence is considered as the obligatory co-existence of an arbitrary subset of possible arguments along with the obligatory exclusion of certain others, relative to a verb sense. Additional morphological lexical and semantic constraints on the syntactic constituents organized as a 5-tier constraint hierarchy, are utilized to map a given syntactic structure case-fraame to a specific verb sense.

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arXiv1995

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.

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arXiv1995

On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms

We consider the problem of learning a certain type of lexical semantic knowledge that can be expressed as a binary relation between words, such as the so-called sub-categorization of verbs (a verb-noun relation) and the compound noun phrase relation (a noun-noun relation). Specifically, we view this problem as an on-line learning problem in the sense of Littlestone's learning model in which the learner's goal is to minimize the total number of prediction mistakes. In the computational learning theory literature, Goldman, Rivest and Schapire and subsequently Goldman and Warmuth have considered the on-line learning problem for binary relations R : X * Y -> {0, 1} in which one of the domain sets X can be partitioned into a relatively small number of types, namely clusters consisting of behaviorally indistinguishable members of X. In this paper, we extend this model and suppose that both of the sets X, Y can be partitioned into a small number of types, and propose a host of prediction algorithms which are two-dimensional extensions of Goldman and Warmuth's weighted majority type algorithm proposed for the original model. We apply these algorithms to the learning problem for the `compound noun phrase' relation, in which a noun is related to another just in case they can form a noun phrase together. Our experimental results show that all of our algorithms out-perform Goldman and Warmuth's algorithm. We also theoretically analyze the performance of one of our algorithms, in the form of an upper bound on the worst case number of prediction mistakes it makes.

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arXiv1996

Generalizing Case Frames Using a Thesaurus and the MDL Principle

We address the problem of automatically acquiring case-frame patterns from large corpus data. In particular, we view this problem as the problem of estimating a (conditional) distribution over a partition of words, and propose a new generalization method based on the MDL (Minimum Description Length) principle. In order to assist with the efficiency, our method makes use of an existing thesaurus and restricts its attention on those partitions that are present as `cuts' in the thesaurus tree, thus reducing the generalization problem to that of estimating the `tree cut models' of the thesaurus. We then give an efficient algorithm which provably obtains the optimal tree cut model for the given frequency data, in the sense of MDL. We have used the case-frame patterns obtained using our method to resolve pp-attachment ambiguity.Our experimental results indicate that our method improves upon or is at least as effective as existing methods.

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arXiv1995

A Compositional Treatment of Polysemous Arguments in Categorial Grammar

We discuss an extension of the standard logical rules (functional application and abstraction) in Categorial Grammar (CG), in order to deal with some specific cases of polysemy. We borrow from Generative Lexicon theory which proposes the mechanism of {\em coercion}, next to a rich nominal lexical semantic structure called {\em qualia structure}. In a previous paper we introduced coercion into the framework of {\em sign-based} Categorial Grammar and investigated its impact on traditional Fregean compositionality. In this paper we will elaborate on this idea, mostly working towards the introduction of a new semantic dimension. Where in current versions of sign-based Categorial Grammar only two representations are derived: a prosodic one (form) and a logical one (modelling), here we introduce also a more detaled representation of the lexical semantics. This extra knowledge will serve to account for linguistic phenomena like {\em metonymy\/}.

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arXiv1995

Bi-Lexical Rules for Multi-Lexeme Translation in Lexicalist MT

The paper presents a prototype lexicalist Machine Translation system (based on the so-called `Shake-and-Bake' approach of Whitelock (1992) consisting of an analysis component, a dynamic bilingual lexicon, and a generation component, and shows how it is applied to a range of MT problems. Multi-Lexeme translations are handled through bi-lexical rules which map bilingual lexical signs into new bilingual lexical signs. It is argued that much translation can be handled by equating translationally equivalent lists of lexical signs, either directly in the bilingual lexicon, or by deriving them through bi-lexical rules. Lexical semantic information organized as Qualia structures (Pustejovsky 1991) is used as a mechanism for restricting the domain of the rules.

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arXiv1995

Heuristics and Parse Ranking

There are currently two philosophies for building grammars and parsers -- Statistically induced grammars and Wide-coverage grammars. One way to combine the strengths of both approaches is to have a wide-coverage grammar with a heuristic component which is domain independent but whose contribution is tuned to particular domains. In this paper, we discuss a three-stage approach to disambiguation in the context of a lexicalized grammar, using a variety of domain independent heuristic techniques. We present a training algorithm which uses hand-bracketed treebank parses to set the weights of these heuristics. We compare the performance of our grammar against the performance of the IBM statistical grammar, using both untrained and trained weights for the heuristics.

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arXiv1995

The Use of Knowledge Preconditions in Language Processing

If an agent does not possess the knowledge needed to perform an action, it may privately plan to obtain the required information on its own, or it may involve another agent in the planning process by engaging it in a dialogue. In this paper, we show how the requirements of knowledge preconditions can be used to account for information-seeking subdialogues in discourse. We first present an axiomatization of knowledge preconditions for the SharedPlan model of collaborative activity (Grosz & Kraus, 1993), and then provide an analysis of information-seeking subdialogues within a general framework for discourse processing. In this framework, SharedPlans and relationships among them are used to model the intentional component of Grosz and Sidner's (1986) theory of discourse structure.

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arXiv1995

Disambiguating bilingual nominal entries against WordNet

This paper explores the acquisition of conceptual knowledge from bilingual dictionaries (French/English, Spanish/English and English/Spanish) using a pre-existing broad coverage Lexical Knowledge Base (LKB) WordNet. Bilingual nominal entries are disambiguated agains WordNet, therefore linking the bilingual dictionaries to WordNet yielding a multilingual LKB (MLKB). The resulting MLKB has the same structure as WordNet, but some nodes are attached additionally to disambiguated vocabulary of other languages. Two different, complementary approaches are explored. In one of the approaches each entry of the dictionary is taken in turn, exploiting the information in the entry itself. The inferential capability for disambiguating the translation is given by Semantic Density over WordNet. In the other approach, the bilingual dictionary was merged with WordNet, exploiting mainly synonymy relations. Each of the approaches was used in a different dictionary. Both approaches attain high levels of precision on their own, showing that disambiguating bilingual nominal entries, and therefore linking bilingual dictionaries to WordNet is a feasible task.

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arXiv1995

Automatic Identification of Support Verbs: A Step Towards a Definition of Semantic Weight

Current definitions of notions of lexical density and semantic weight are based on the division of words into closed and open classes, and on intuition. This paper develops a computationally tractable definition of semantic weight, concentrating on what it means for a word to be semantically light; the definition involves looking at the frequency of a word in particular syntactic constructions which are indicative of lightness. Verbs such as "make" and "take", when they function as support verbs, are often considered to be semantically light. To test our definition, we carried out an experiment based on that of Grefenstette and Teufel (1995), where we automatically identify light instances of these words in a corpus; this was done by incorporating our frequency-related definition of semantic weight into a statistical approach similar to that of Grefenstette and Teufel. The results show that this is a plausible definition of semantic lightness for verbs, which can possibly be extended to defining semantic lightness for other classes of words.

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arXiv1995

Toward an MT System without Pre-Editing --- Effects of New Methods in ALT-J/E ---

Recently, several types of Japanese-to-English machine translation systems have been developed, but all of them require an initial process of rewriting the original text into easily translatable Japanese. Therefore these systems are unsuitable for translating information that needs to be speedily disseminated. To overcome this limitation, a Multi-Level Translation Method based on the Constructive Process Theory has been proposed. This paper describes the benefits of using this method in the Japanese-to-English machine translation system ALT-J/E. In comparison with conventional compositional methods, the Multi-Level Translation Method emphasizes the importance of the meaning contained in expression structures as a whole. It is shown to be capable of translating typical written Japanese based on the meaning of the text in its context, with comparative ease. We are now hopeful of carrying out useful machine translation with no manual pre-editing.

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arXiv1995

Countability and Number in Japanese-to-English Machine Translation

This paper presents a heuristic method that uses information in the Japanese text along with knowledge of English countability and number stored in transfer dictionaries to determine the countability and number of English noun phrases. Incorporating this method into the machine translation system ALT-J/E, helped to raise the percentage of noun phrases generated with correct use of articles and number from 65% to 73%.

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arXiv1995

Using Information Content to Evaluate Semantic Similarity in a Taxonomy

This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66).

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arXiv1995

The Unsupervised Acquisition of a Lexicon from Continuous Speech

We present an unsupervised learning algorithm that acquires a natural-language lexicon from raw speech. The algorithm is based on the optimal encoding of symbol sequences in an MDL framework, and uses a hierarchical representation of language that overcomes many of the problems that have stymied previous grammar-induction procedures. The forward mapping from symbol sequences to the speech stream is modeled using features based on articulatory gestures. We present results on the acquisition of lexicons and language models from raw speech, text, and phonetic transcripts, and demonstrate that our algorithm compares very favorably to other reported results with respect to segmentation performance and statistical efficiency.

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arXiv1996

Limited Attention and Discourse Structure

This squib examines the role of limited attention in a theory of discourse structure and proposes a model of attentional state that relates current hierarchical theories of discourse structure to empirical evidence about human discourse processing capabilities. First, I present examples that are not predicted by Grosz and Sidner's stack model of attentional state. Then I consider an alternative model of attentional state, the cache model, which accounts for the examples, and which makes particular processing predictions. Finally I suggest a number of ways that future research could distinguish the predictions of the cache model and the stack model.

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