Articles

Many recent statistical parsers rely on a preprocess- ing step which uses hand-written, corpus-specific rules to augment the training data with extra infor- mation. For example, head-finding rules are used to augment node labels with lexical heads. In this paper, we provide machinery to reduce the amount of human effort …...

We propose a novel measure of the representativeness (i.e., indicativeness or topic specificity) of a term in a given corpus. The measure embodies the idea that the distribution of words co-occurring with a representative term should be biased according to the word distribution in the whole corpus. The bias of …...

This paper proposes a method of fertilizing a Japanese case frame dictionary to handle com- plicated expressions: double nominative sen- tences, non-gapping relation of relative clauses, and case change. Our method is divided into two stages. In the first stage, we parse a large corpus and construct a Japanese case …...

Zipf’s law states that the frequency of word tokens in a large corpus of natural language is inversely proportional to the rank. The law is investigated for two languages English and Mandarin and for n- gram word phrases as well as for single words. The law for single words is …...

We propose a formal characterization of variation in the syntactic realization of semantic arguments, using hierarchies of syntactic relations and thematic roles, and a mechanism of lexical inheritance to ob- tain valency frames from individual linking types. We embed the formalization in the new lexicalized, dependency-based grammar formalism of Topologi- …...

We present an engine for text adventures – computer games with which the player interacts using natu- ral language. The system employs current meth- ods from computational linguistics and an efficient inference system for description logic to make the interaction more natural. The inference system is especially useful in the …...

This paper explores the contribution of a broad range of syntactic features to WSD: grammatical relations coded as the presence of adjuncts/arguments in isolation or as subcategorization frames, and instantiated grammatical relations between words. We have tested the performance of syntactic features using two different ML algorithms (Decision Lists and …...

This paper presents an unsupervised method for assembling semantic knowledge from a part-of- speech tagged corpus using graph algorithms. The graph model is built by linking pairs of words which participate in particular syntactic relationships. We focus on the symmetric rela- tionship between pairs of nouns which occur to- gether …...

This paper proposes a method of speech inten- tion understanding based on dialogue examples. The method uses a spoken dialogue corpus with intention tags to regard the intention of each in- put utterance as that of the sentence to which it is the most similar in the corpus. The degree …...

We present in this paper a technique allowing to choose the parsing granularity within the same approach relying on a constraint-based formalism. Its main advantage lies in the fact that the same linguistic resources are used whatever the granularity. Such a method is useful in particular for systems such as …...

Automatic text categorization is a problem of automatically assigning text documents to predefined categories. In order to classify text documents, we must extract good features from them. In previous research, a text document is commonly represented by the term frequency and the inverted document frequency of each feature. Since there …...

While the corpus-based research relies on hu- man annotated corpora, it is often said that a non-negligible amount of errors remain even in frequently used corpora such as Penn Treebank. Detection of errors in annotated corpora is im- portant for corpus-based natural language pro- cessing. In this paper, we propose …...

This paper describes new default unification, lenient default unification. It works efficiently, and gives more informative results because it maximizes the amount of information in the result, while other de- fault unification maximizes it in the default. We also describe robust processing within the framework of HPSG. We extract grammar …...

There is increasing concern about English-Korean (E-K) transliteration recently. In the previous works, direct converting methods from English alphabets to Korean alphabets were a main research topic. In this paper, we present an E-K transliteration model using pronunciation and contextual rules. Unlike the previous works, our method uses phonetic information …...

This paper presents techniques for multimedia annotation and their application to video sum- marization and translation. Our tool for anno- tation allows users to easily create annotation including voice transcripts, video scene descrip- tions, and visual/auditory object descriptions. The module for voice transcription is capable of multilingual spoken language identification …...

It is popular in WSD to use contextual information in training sense tagged data. Co-occurring words within a limited window-sized context support one sense among the semantically ambiguous ones of the word. This paper reports on word sense disambiguation of English words using static and dynamic sense vectors. First, context …...

In word prediction systems for augmentative and al- ternative communication (AAC), productive word- formation processes such as compounding pose a serious problem. We present a model that predicts German nominal compounds by splitting them into their modifier and head components, instead of try- ing to predict them as a whole. …...

The paper examines different possibilities to take advantage of the taxonomic or- ganization of a thesaurus to improve the accuracy of classifying new words into its classes. The results of the study demon- strate that taxonomic similarity between nearest neighbors, in addition to their dis- tributional similarity to the new …...

In this paper we propose an integration of a selforganizing map and semantic networks from WordNet for a text classification task using the new Reuters news corpus. This neural model is based on significance vectors and benefits from the presentation of document clusters. The Hypernym relation in WordNet supplements the …...

This paper proposes an unsupervised learning model for classifying named entities. This model uses a training set, built automatically by means of a small-scale named entity dictionary and an unlabeled corpus. This enables us to classify named entities without the cost for building a large hand-tagged training corpus or a …...