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arXiv1996

Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean

A new tightly coupled speech and natural language integration model is presented for a TDNN-based continuous possibly large vocabulary speech recognition system for Korean. Unlike popular n-best techniques developed for integrating mainly HMM-based speech recognition and natural language processing in a {\em word level}, which is obviously inadequate for morphologically complex agglutinative languages, our model constructs a spoken language system based on a {\em morpheme-level} speech and language integration. With this integration scheme, the spoken Korean processing engine (SKOPE) is designed and implemented using a TDNN-based diphone recognition module integrated with a Viterbi-based lexical decoding and symbolic phonological/morphological co-analysis. Our experiment results show that the speaker-dependent continuous {\em eojeol} (Korean word) recognition and integrated morphological analysis can be achieved with over 80.6% success rate directly from speech inputs for the middle-level vocabularies.

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arXiv1996

Extraction of V-N-Collocations from Text Corpora: A Feasibility Study for German

The usefulness of a statistical approach suggested by Church et al. (1991) is evaluated for the extraction of verb-noun (V-N) collocations from German text corpora. Some problematic issues of that method arising from properties of the German language are discussed and various modifications of the method are considered that might improve extraction results for German. The precision and recall of all variant methods is evaluated for V-N collocations containing support verbs, and the consequences for further work on the extraction of collocations from German corpora are discussed. With a sufficiently large corpus (>= 6 mio. word-tokens), the average error rate of wrong extractions can be reduced to 2.2% (97.8% precision) with the most restrictive method, however with a loss in data of almost 50% compared to a less restrictive method with still 87.6% precision. Depending on the goal to be achieved, emphasis can be put on a high recall for lexicographic purposes or on high precision for automatic lexical acquisition, in each case unfortunately leading to a decrease of the corresponding other variable. Low recall can still be acceptable if very large corpora (i.e. 50 - 100 million words) are available or if corpora for special domains are used in addition to the data found in machine readable (collocation) dictionaries.

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arXiv1996

A Constraint-based Case Frame Lexicon

We present a constraint-based case frame lexicon architecture for bi-directional mapping between a syntactic case frame and a semantic frame. The lexicon uses a semantic sense as the basic unit and employs a multi-tiered constraint structure for the resolution of syntactic information into the appropriate senses and/or idiomatic usage. Valency changing transformations such as morphologically marked passivized or causativized forms are handled via lexical rules that manipulate case frames templates. The system has been implemented in a typed-feature system and applied to Turkish.

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arXiv1996

Apportioning Development Effort in a Probabilistic LR Parsing System through Evaluation

We describe an implemented system for robust domain-independent syntactic parsing of English, using a unification-based grammar of part-of-speech and punctuation labels coupled with a probabilistic LR parser. We present evaluations of the system's performance along several different dimensions; these enable us to assess the contribution that each individual part is making to the success of the system as a whole, and thus prioritise the effort to be devoted to its further enhancement. Currently, the system is able to parse around 80% of sentences in a substantial corpus of general text containing a number of distinct genres. On a random sample of 250 such sentences the system has a mean crossing bracket rate of 0.71 and recall and precision of 83% and 84% respectively when evaluated against manually-disambiguated analyses.

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arXiv1996

Another Facet of LIG Parsing

In this paper we present a new parsing algorithm for linear indexed grammars (LIGs) in the same spirit as the one described in (Vijay-Shanker and Weir, 1993) for tree adjoining grammars. For a LIG $L$ and an input string $x$ of length $n$, we build a non ambiguous context-free grammar whose sentences are all (and exclusively) valid derivation sequences in $L$ which lead to $x$. We show that this grammar can be built in ${\cal O}(n^6)$ time and that individual parses can be extracted in linear time with the size of the extracted parse tree. Though this ${\cal O}(n^6)$ upper bound does not improve over previous results, the average case behaves much better. Moreover, practical parsing times can be decreased by some statically performed computations.

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arXiv1996

Unsupervised Learning of Word-Category Guessing Rules

Words unknown to the lexicon present a substantial problem to part-of-speech tagging. In this paper we present a technique for fully unsupervised statistical acquisition of rules which guess possible parts-of-speech for unknown words. Three complementary sets of word-guessing rules are induced from the lexicon and a raw corpus: prefix morphological rules, suffix morphological rules and ending-guessing rules. The learning was performed on the Brown Corpus data and rule-sets, with a highly competitive performance, were produced and compared with the state-of-the-art.

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arXiv1996

Yet Another Paper about Partial Verb Phrase Fronting in German

I describe a very simple HPSG analysis for partial verb phrase fronting. I will argue that the presented account is more adequate than others made during the past years because it allows the description of constituents in fronted positions with their modifier remaining in the non-fronted part of the sentence. A problem with ill-formed signs that are admitted by all HPSG accounts for partial verb phrase fronting known so far will be explained and a solution will be suggested that uses the difference between combinatoric relations of signs and their representation in word order domains.

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arXiv1996

Resolving Anaphors in Embedded Sentences

We propose an algorithm to resolve anaphors, tackling mainly the problem of intrasentential antecedents. We base our methodology on the fact that such antecedents are likely to occur in embedded sentences. Sidner's focusing mechanism is used as the basic algorithm in a more complete approach. The proposed algorithm has been tested and implemented as a part of a conceptual analyser, mainly to process pronouns. Details of an evaluation are given.

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arXiv1996

Adapting the Core Language Engine to French and Spanish

We describe how substantial domain-independent language-processing systems for French and Spanish were quickly developed by manually adapting an existing English-language system, the SRI Core Language Engine. We explain the adaptation process in detail, and argue that it provides a fairly general recipe for converting a grammar-based system for English into a corresponding one for a Romance language.

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arXiv1996

Parsing for Semidirectional Lambek Grammar is NP-Complete

We study the computational complexity of the parsing problem of a variant of Lambek Categorial Grammar that we call {\em semidirectional}. In semidirectional Lambek calculus $\SDL$ there is an additional non-directional abstraction rule allowing the formula abstracted over to appear anywhere in the premise sequent's left-hand side, thus permitting non-peripheral extraction. $\SDL$ grammars are able to generate each context-free language and more than that. We show that the parsing problem for semidirectional Lambek Grammar is NP-complete by a reduction of the 3-Partition problem.

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arXiv1996

A Simple Transformation for Offline-Parsable Grammars and its Termination Properties

We present, in easily reproducible terms, a simple transformation for offline-parsable grammars which results in a provably terminating parsing program directly top-down interpretable in Prolog. The transformation consists in two steps: (1) removal of empty-productions, followed by: (2) left-recursion elimination. It is related both to left-corner parsing (where the grammar is compiled, rather than interpreted through a parsing program, and with the advantage of guaranteed termination in the presence of empty productions) and to the Generalized Greibach Normal Form for DCGs (with the advantage of implementation simplicity).

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arXiv1996

Towards Understanding Spontaneous Speech: Word Accuracy vs. Concept Accuracy

In this paper we describe an approach to automatic evaluation of both the speech recognition and understanding capabilities of a spoken dialogue system for train time table information. We use word accuracy for recognition and concept accuracy for understanding performance judgement. Both measures are calculated by comparing these modules' output with a correct reference answer. We report evaluation results for a spontaneous speech corpus with about 10000 utterances. We observed a nearly linear relationship between word accuracy and concept accuracy.

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arXiv1996

Efficient Normal-Form Parsing for Combinatory Categorial Grammar

Under categorial grammars that have powerful rules like composition, a simple n-word sentence can have exponentially many parses. Generating all parses is inefficient and obscures whatever true semantic ambiguities are in the input. This paper addresses the problem for a fairly general form of Combinatory Categorial Grammar, by means of an efficient, correct, and easy to implement normal-form parsing technique. The parser is proved to find exactly one parse in each semantic equivalence class of allowable parses; that is, spurious ambiguity (as carefully defined) is shown to be both safely and completely eliminated.

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arXiv1996

Word Sense Disambiguation using Conceptual Density

This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. The method relies on the use of the wide-coverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a Conceptual Density formula developed for this purpose. This fully automatic method requires no hand coding of lexical entries, hand tagging of text nor any kind of training process. The results of the experiments have been automatically evaluated against SemCor, the sense-tagged version of the Brown Corpus.

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arXiv1996

An Empirical Study of Smoothing Techniques for Language Modeling

We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e.g., Brown versus Wall Street Journal), and n-gram order (bigram versus trigram) affect the relative performance of these methods, which we measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods.

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arXiv1996

An Efficient Inductive Unsupervised Semantic Tagger

We report our development of a simple but fast and efficient inductive unsupervised semantic tagger for Chinese words. A POS hand-tagged corpus of 348,000 words is used. The corpus is being tagged in two steps. First, possible semantic tags are selected from a semantic dictionary(Tong Yi Ci Ci Lin), the POS and the conditional probability of semantic from POS, i.e., P(S|P). The final semantic tag is then assigned by considering the semantic tags before and after the current word and the semantic-word conditional probability P(S|W) derived from the first step. Semantic bigram probabilities P(S|S) are used in the second step. Final manual checking shows that this simple but efficient algorithm has a hit rate of 91%. The tagger tags 142 words per second, using a 120 MHz Pentium running FOXPRO. It runs about 2.3 times faster than a Viterbi tagger.

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arXiv1996

Building Probabilistic Models for Natural Language

In this thesis, we investigate three problems involving the probabilistic modeling of language: smoothing n-gram models, statistical grammar induction, and bilingual sentence alignment. These three problems employ models at three different levels of language; they involve word-based, constituent-based, and sentence-based models, respectively. We describe techniques for improving the modeling of language at each of these levels, and surpass the performance of existing algorithms for each problem. We approach the three problems using three different frameworks. We relate each of these frameworks to the Bayesian paradigm, and show why each framework used was appropriate for the given problem. Finally, we show how our research addresses two central issues in probabilistic modeling: the sparse data problem and the problem of inducing hidden structure.

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arXiv1996

With raised eyebrows or the eyebrows raised ? A Neural Network Approach to Grammar Checking for Definiteness

In this paper, we use a feature model of the semantics of plural determiners to present an approach to grammar checking for definiteness. Using neural network techniques, a semantics -- morphological category mapping was learned. We then applied a textual encoding technique to the 125 occurences of the relevant category in a 10 000 word narrative text and learned a surface -- semantics mapping. By applying the learned generation function to the newly generated representations, we achieved a correct category assignment in many cases (87 %). These results are considerably better than a direct surface categorization approach (54 %), with a baseline (always guessing the dominant category) of 60 %. It is discussed, how these results could be used in multilingual NLP applications.

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