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In this paper, we propose a robust parser which can parse extragrammatical sentences. This parser can recover them using only syntactic information. It can be easily modified and extended because it utilize only syntactic information.
The paper presents a constraint based semantic formalism for HPSG. The syntax-semantics interface directly implements syntactic conditions on quantifier scoping and distributivity. The construction of semantic representations is guided by general principles governing the interaction between syntax and semantics. Each of these principles acts as a constraint to narrow down the set of possible interpretations of a sentence. Meanings of ambiguous sentences are represented by single partial representations (so-called U(nderspecified) D(iscourse) R(epresentation) S(tructure)s) to which further constraints can be added monotonically to gain more information about the content of a sentence. There is no need to build up a large number of alternative representations of the sentence which are then filtered by subsequent discourse and world knowledge. The advantage of UDRSs is not only that they allow for monotonic incremental interpretation but also that they are equipped with truth conditions and a proof theory that allows for inferences to be drawn directly on structures where quantifier scope is not resolved.
The paper adresses the problem of reasoning with ambiguities. Semantic representations are presented that leave scope relations between quantifiers and/or other operators unspecified. Truth conditions are provided for these representations and different consequence relations are judged on the basis of intuitive correctness. Finally inference patterns are presented that operate directly on these underspecified structures, i.e. do not rely on any translation into the set of their disambiguations.
This paper investigates the syntax of extraposition in the HPSG framework. We present English and German data (partly taken from corpora), and provide an analysis using lexical rules and a nonlocal dependency. The condition for binding this dependency is formulated relative to the antecedent of the extraposed phrase, which entails that no fixed site for extraposition exists. Our analysis accounts for the interaction of extraposition with fronting and coordination, and predicts constraints on multiple extraposition.
This paper describes a method to automatically acquire the syntactic and semantic classifications of unknown words. Our method reduces the search space of the lexical acquisition problem by utilizing both the left and the right context of the unknown word. Link Grammar provides a convenient framework in which to implement our method.
This paper proposes a corpus-based language model for topic identification. We analyze the association of noun-noun and noun-verb pairs in LOB Corpus. The word association norms are based on three factors: 1) word importance, 2) pair co-occurrence, and 3) distance. They are trained on the paragraph and sentence levels for noun-noun and noun-verb pairs, respectively. Under the topic coherence postulation, the nouns that have the strongest connectivities with the other nouns and verbs in the discourse form the preferred topic set. The collocational semantics then is used to identify the topics from paragraphs and to discuss the topic shift phenomenon among paragraphs.
A bi-directional Korean/English dialog translation system is designed and implemented using the memory-based translation technique. The system KEMDT (Korean/English Memory-based Dialog Translation system) can perform Korean to English, and English to Korean translation using unified memory network and extended marker passing algorithm. We resolve the word order variation and frequent word omission problems in Korean by classifying the concept sequence element in four different types and extending the marker- passing-based-translation algorithm. Unlike the previous memory-based translation systems, the KEMDT system develops the bilingual memory network and the unified bi-directional marker passing translation algorithm. For efficient language specific processing, we separate the morphological processors from the memory-based translator. The KEMDT technology provides a hierarchical memory network and an efficient marker-based control for the recent example-based MT paradigm.
This paper is concerned with the detection and correction of sub-sentential English text errors. Previous spelling programs, unless restricted to a very small set of words, have operated as post-processors. And to date, grammar checkers and other programs which deal with ill-formed input usually step directly from spelling considerations to a full-scale parse, assuming a complete sentence. Work described below is aimed at evaluating the effectiveness of shallow (sub-sentential) processing and the feasibility of cooperative error checking, through building and testing appropriately an error-processing system. A system under construction is outlined which incorporates morphological checks (using new two-level error rules) over a directed letter graph, tag positional trigrams and partial parsing. Intended testing is discussed.
The work reported here is the result of a study done within a larger project on the ``Semantics of Natural Languages'' viewed from the field of Artificial Intelligence and Computational Linguistics. In this project, we have chosen a corpus of insurance claim reports. These texts deal with a relatively circumscribed domain, that of road traffic, thereby limiting the extra-linguistic knowledge necessary to understand them. Moreover, these texts present a number of very specific characteristics, insofar as they are written in a quasi-institutional setting which imposes many constraints on their production. We first determine what these constraints are in order to then show how they provide the writer with the means to create as succint a text as possible, and in a symmetric way, how they provide the reader with the means to interpret the text and to distinguish between its factual and argumentative aspects.
This paper concerns both anaphora resolution and prepositional phrase (PP) attachment that are the most frequent ambiguities in natural language processing. Several methods have been proposed to deal with each phenomenon separately, however none of proposed systems has considered the way of dealing both phenomena. We tackle this issue, proposing an algorithm to co-ordinate the treatment of these two problems efficiently, i.e., the aim is also to exploit at each step all the results that each component can provide.
Literal movement grammars (LMGs) provide a general account of extraposition phenomena through an attribute mechanism allowing top-down displacement of syntactical information. LMGs provide a simple and efficient treatment of complex linguistic phenomena such as cross-serial dependencies in German and Dutch---separating the treatment of natural language into a parsing phase closely resembling traditional context-free treatment, and a disambiguation phase which can be carried out using matching, as opposed to full unification employed in most current grammar formalisms of linguistical relevance.
This paper presents a grammar formalism designed for use in data-oriented approaches to language processing. The formalism is best described as a right-linear indexed grammar extended in linguistically interesting ways. The paper goes on to investigate how a corpus pre-parsed with this formalism may be processed to provide a probabilistic language model for use in the parsing of fresh texts.
A German language model for the Xerox HMM tagger is presented. This model's performance is compared with two other German taggers with partial parameter re-estimation and full adaption of parameters from pre-tagged corpora. The ambiguity types resolved by this model are analysed and compared to ambiguity types of English and French. Finally, the model's error types are described. I argue that although the overall performance of these models for German is comparable to results for English and French, a more exact analysis demonstrates important differences in the types of disambiguation involved for German.
In this paper, we describe an approach to sentence categorization which has the originality to be based on natural properties of languages with no training set dependency. The implementation is fast, small, robust and textual errors tolerant. Tested for french, english, spanish and german discrimination, the system gives very interesting results, achieving in one test 99.4% correct assignments on real sentences. The resolution power is based on grammatical words (not the most common words) and alphabet. Having the grammatical words and the alphabet of each language at its disposal, the system computes for each of them its likelihood to be selected. The name of the language having the optimum likelihood will tag the sentence --- but non resolved ambiguities will be maintained. We will discuss the reasons which lead us to use these linguistic facts and present several directions to improve the system's classification performance. Categorization sentences with linguistic properties shows that difficult problems have sometimes simple solutions.
This paper reports on the preliminary phase of our ongoing research towards developing an intelligent tutoring environment for Turkish grammar. One of the components of this environment is a corpus search tool which, among other aspects of the language, will be used to present the learner sample sentences along with their morphological analyses. Following a brief introduction to the Turkish language and its morphology, the paper describes the morphological analysis and ambiguity resolution used to construct the corpus used in the search tool. Finally, implementation issues and details involving the user interface of the tool are discussed.
Dialect groupings can be discovered objectively and automatically by cluster analysis of phonetic transcriptions such as those found in a linguistic atlas. The first step in the analysis, the computation of linguistic distance between each pair of sites, can be computed as Levenshtein distance between phonetic strings. This correlates closely with the much more laborious technique of determining and counting isoglosses, and is more accurate than the more familiar metric of computing Hamming distance based on whether vocabulary entries match. In the actual clustering step, traditional agglomerative clustering works better than the top-down technique of partitioning around medoids. When agglomerative clustering of phonetic string comparison distances is applied to Gaelic, reasonable dialect boundaries are obtained, corresponding to national and (within Ireland) provincial boundaries.
In this paper we compare two competing approaches to part-of-speech tagging, statistical and constraint-based disambiguation, using French as our test language. We imposed a time limit on our experiment: the amount of time spent on the design of our constraint system was about the same as the time we used to train and test the easy-to-implement statistical model. We describe the two systems and compare the results. The accuracy of the statistical method is reasonably good, comparable to taggers for English. But the constraint-based tagger seems to be superior even with the limited time we allowed ourselves for rule development.
We earlier described two taggers for French, a statistical one and a constraint-based one. The two taggers have the same tokeniser and morphological analyser. In this paper, we describe aspects of this work concerned with the definition of the tagset, the building of the lexicon, derived from an existing two-level morphological analyser, and the definition of a lexical transducer for guessing unknown words.