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arXiv1995

Robust Parsing Based on Discourse Information: Completing partial parses of ill-formed sentences on the basis of discourse information

In a consistent text, many words and phrases are repeatedly used in more than one sentence. When an identical phrase (a set of consecutive words) is repeated in different sentences, the constituent words of those sentences tend to be associated in identical modification patterns with identical parts of speech and identical modifiee-modifier relationships. Thus, when a syntactic parser cannot parse a sentence as a unified structure, parts of speech and modifiee-modifier relationships among morphologically identical words in complete parses of other sentences within the same text provide useful information for obtaining partial parses of the sentence. In this paper, we describe a method for completing partial parses by maintaining consistency among morphologically identical words within the same text as regards their part of speech and their modifiee-modifier relationship. The experimental results obtained by using this method with technical documents offer good prospects for improving the accuracy of sentence analysis in a broad-coverage natural language processing system such as a machine translation system.

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arXiv1995

Weak subsumption Constraints for Type Diagnosis: An Incremental Algorithm

We introduce constraints necessary for type checking a higher-order concurrent constraint language, and solve them with an incremental algorithm. Our constraint system extends rational unification by constraints x$\subseteq$ y saying that ``$x$ has at least the structure of $y$'', modelled by a weak instance relation between trees. This notion of instance has been carefully chosen to be weaker than the usual one which renders semi-unification undecidable. Semi-unification has more than once served to link unification problems arising from type inference and those considered in computational linguistics. Just as polymorphic recursion corresponds to subsumption through the semi-unification problem, our type constraint problem corresponds to weak subsumption of feature graphs in linguistics. The decidability problem for \WhatsIt for feature graphs has been settled by Dörre~\cite{Doerre:WeakSubsumption:94}. \nocite{RuppRosnerJohnson:94} In contrast to Dörre's, our algorithm is fully incremental and does not refer to finite state automata. Our algorithm also is a lot more flexible. It allows a number of extensions (records, sorts, disjunctive types, type declarations, and others) which make it suitable for type inference of a full-fledged programming language.

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arXiv1995

Using Higher-Order Logic Programming for Semantic Interpretation of Coordinate Constructs

Many theories of semantic interpretation use lambda-term manipulation to compositionally compute the meaning of a sentence. These theories are usually implemented in a language such as Prolog that can simulate lambda-term operations with first-order unification. However, for some interesting cases, such as a Combinatory Categorial Grammar account of coordination constructs, this can only be done by obscuring the underlying linguistic theory with the ``tricks'' needed for implementation. This paper shows how the use of abstract syntax permitted by higher-order logic programming allows an elegant implementation of the semantics of Combinatory Categorial Grammar, including its handling of coordination constructs.

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arXiv1995

A Support Tool for Tagset Mapping

Many different tagsets are used in existing corpora; these tagsets vary according to the objectives of specific projects (which may be as far apart as robust parsing vs. spelling correction). In many situations, however, one would like to have uniform access to the linguistic information encoded in corpus annotations without having to know the classification schemes in detail. This paper describes a tool which maps unstructured morphosyntactic tags to a constraint-based, typed, configurable specification language, a ``standard tagset''. The mapping relies on a manually written set of mapping rules, which is automatically checked for consistency. In certain cases, unsharp mappings are unavoidable, and noise, i.e. groups of word forms {\sl not} conforming to the specification, will appear in the output of the mapping. The system automatically detects such noise and informs the user about it. The tool has been tested with rules for the UPenn tagset \cite{up} and the SUSANNE tagset \cite{garside}, in the framework of the EAGLES\footnote{LRE project EAGLES, cf. \cite{eagles}.} validation phase for standardised tagsets for European languages.

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arXiv1995

Automatic Extraction of Tagset Mappings from Parallel-Annotated Corpora

This paper describes some of the recent work of project AMALGAM (automatic mapping among lexico-grammatical annotation models). We are investigating ways to map between the leading corpus annotation schemes in order to improve their resuability. Collation of all the included corpora into a single large annotated corpus will provide a more detailed language model to be developed for tasks such as speech and handwriting recognition. In particular, we focus here on a method of extracting mappings from corpora that have been annotated according to more than one annotation scheme.

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arXiv1995

Unification-Based Glossing

We present an approach to syntax-based machine translation that combines unification-style interpretation with statistical processing. This approach enables us to translate any Japanese newspaper article into English, with quality far better than a word-for-word translation. Novel ideas include the use of feature structures to encode word lattices and the use of unification to compose and manipulate lattices. Unification also allows us to specify abstract features that delay target-language synthesis until enough source-language information is assembled. Our statistical component enables us to search efficiently among competing translations and locate those with high English fluency.

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arXiv1995

A Study of the Context(s) in a Specific Type of Texts: Car Accident Reports

This paper addresses the issue of defining context, and more specifically the different contexts needed for understanding a particular type of texts. The corpus chosen is homogeneous and allows us to determine characteristic properties of the texts from which certain inferences can be drawn by the reader. These characteristic properties come from the real world domain (K-context), the type of events the texts describe (F-context) and the genre of the texts (E-context). Together, these three contexts provide elements for the resolution of anaphoric expressions and for several types of disambiguation. We show in particular that the argumentation aspect of these texts is an essential part of the context and explains some of the inferences that can be drawn.

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arXiv1995

Inducing Features of Random Fields

We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The statistical modeling techniques introduced in this paper differ from those common to much of the natural language processing literature since there is no probabilistic finite state or push-down automaton on which the model is built. Our approach also differs from the techniques common to the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classification in natural language processing. Key words: random field, Kullback-Leibler divergence, iterative scaling, divergence geometry, maximum entropy, EM algorithm, statistical learning, clustering, word morphology, natural language processing

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arXiv1995

Ambiguity in the Acquisition of Lexical Information

This paper describes an approach to the automatic identification of lexical information in on-line dictionaries. This approach uses bootstrapping techniques, specifically so that ambiguity in the dictionary text can be treated properly. This approach consists of processing an on-line dictionary multiple times, each time refining the lexical information previously acquired and identifying new lexical information. The strength of this approach is that lexical information can be acquired from definitions which are syntactically ambiguous, given that information acquired during the first pass can be used to improve the syntactic analysis of definitions in subsequent passes. In the context of a lexical knowledge base, the types of lexical information that need to be represented cannot be viewed as a fixed set, but rather as a set that will change given the resources of the lexical knowledge base and the requirements of analysis systems which access it.

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arXiv1995

Indefeasible Semantics and Defeasible Pragmatics

An account of utterance interpretation in discourse needs to face the issue of how the discourse context controls the space of interacting preferences. Assuming a discourse processing architecture that distinguishes the grammar and pragmatics subsystems in terms of monotonic and nonmonotonic inferences, I will discuss how independently motivated default preferences interact in the interpretation of intersentential pronominal anaphora. In the framework of a general discourse processing model that integrates both the grammar and pragmatics subsystems, I will propose a fine structure of the preferential interpretation in pragmatics in terms of defeasible rule interactions. The pronoun interpretation preferences that serve as the empirical ground draw from the survey data specifically obtained for the present purpose.

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arXiv1995

The Effect of Pitch Accenting on Pronoun Referent Resolution

By strictest interpretation, theories of both centering and intonational meaning fail to predict the existence of pitch accented pronominals. Yet they occur felicitously in spoken discourse. To explain this, I emphasize the dual functions served by pitch accents, as markers of both propositional (semantic/pragmatic) and attentional salience. This distinction underlies my proposals about the attentional consequences of pitch accents when applied to pronominals, in particular, that while most pitch accents may weaken or reinforce a cospecifier's status as the center of attention, a contrastively stressed pronominal may force a shift, even when contraindicated by textual features.

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arXiv1995

Review of Charniak's "Statistical Language Learning"

This article is an in-depth review of Eugene Charniak's book, "Statistical Language Learning". The review evaluates the appropriateness of the book as an introductory text for statistical language learning for a variety of audiences. It also includes an extensive bibliography of articles and papers which might be used as a supplement to this book for learning or teaching statistical language modeling.

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arXiv1995

Deriving Procedural and Warning Instructions from Device and Environment Models

This study is centred on the generation of instructions for household appliances. We show how knowledge about a device, together with knowledge about the environment, can be used for reasoning about instructions. The information communicated by the instructions can be planned from a version of the knowledge of the artifact and environment. We present the latter, which we call the {\it planning knowledge}, in the form of axioms in the {\it situation calculus}. This planning knowledge formally characterizes the behaviour of the artifact, and it is used to produce a basic plan of actions that the device and user take to accomplish a given goal. We explain how both procedural and warning instructions can be generated from this basic plan. In order to partially justify that instruction generation can be automated from a formal device design specification, we assume that the planning knowledge is {\it derivable\/} from the device and world knowledge.

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arXiv1995

An Approach to Proper Name Tagging for German

This paper presents an incremental method for the tagging of proper names in German newspaper texts. The tagging is performed by the analysis of the syntactic and textual contexts of proper names together with a morphological analysis. The proper names selected by this process supply new contexts which can be used for finding new proper names, and so on. This procedure was applied to a small German corpus (50,000 words) and correctly disambiguated 65% of the capitalized words, which should improve when it is applied to a very large corpus.

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arXiv1995

A Categorial Framework for Composition in Multiple Linguistic Domains

This paper describes a computational framework for a grammar architecture in which different linguistic domains such as morphology, syntax, and semantics are treated not as separate components but compositional domains. Word and phrase formation are modeled as uniform processes contributing to the derivation of the semantic form. The morpheme, as well as the lexeme, has lexical representation in the form of semantic content, tactical constraints, and phonological realization. The motivation for this work is to handle morphology-syntax interaction (e.g., valency change in causatives, subcategorization imposed by case-marking affixes) in an incremental way. The model is based on Combinatory Categorial Grammars.

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arXiv1995

A framework for lexical representation

In this paper we present a unification-based lexical platform designed for highly inflected languages (like Roman ones). A formalism is proposed for encoding a lemma-based lexical source, well suited for linguistic generalizations. From this source, we automatically generate an allomorph indexed dictionary, adequate for efficient processing. A set of software tools have been implemented around this formalism: access libraries, morphological processors, etc.

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arXiv1995

Robust Processing of Natural Language

Previous approaches to robustness in natural language processing usually treat deviant input by relaxing grammatical constraints whenever a successful analysis cannot be provided by ``normal'' means. This schema implies, that error detection always comes prior to error handling, a behaviour which hardly can compete with its human model, where many erroneous situations are treated without even noticing them. The paper analyses the necessary preconditions for achieving a higher degree of robustness in natural language processing and suggests a quite different approach based on a procedure for structural disambiguation. It not only offers the possibility to cope with robustness issues in a more natural way but eventually might be suited to accommodate quite different aspects of robust behaviour within a single framework.

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