Fumiyo Fukumoto

Event tracking based on domain dependency

This paper proposes a method for event tracking on broadcast news stories based on distinction between a topic and an event. A topic and an event are identified using a simple criterion called domain dependency of words: how greatly a word features a given set of data. The method was tested on the TDT corpus which has been developed by the TDT Pilot Study and the result can be regarded as promising the usefulness of the method.

An automatic clustering of articles using dictionary definitions

In this paper, we propose a statistical approach for clustering of articles using on-line dictionary definitions. One of the characteristics of our approach is that every sense of word in articles is automatically disambiguated using dictionary definitions. The other is that in order to cope with the problem of a phrasal lexicon, linking which links words with their semantically similar words in articles is introduced in our method. The results of experiments demonstrate the effectiveness of the proposed method.

Keyword Extraction using Term-Domain Interdependence for Dictation of Radio News

In this paper, we propose keyword extraction method for dictation of radio news which consists of several domains. In our method, newspaper articles which are automatically classified into suitable domains are used in order to calculate feature vectors. The feature vectors shows term-domain interdependence and are used for selecting a suitable domain of each part of radio news. Keywords are extracted by using the selected domain. The results of keyword extraction experiments showed that our methods are robust and effective for dictation of radio news.

An Automatic Extraction o f Key Paragraphs Based on Context Dependency

In this paper, we propose a method for extracting key paragraphs in articles based on the degree of context dependency. Like Luhns technique, our method assumes that the words related to theme in an article appear throughout paragraphs. Our extraction technique of keywords is based on the degree of context dependency that how strongly a word is related to a given context. The results of experiments demonstrate the applicability of our proposed method.

Multi-document summarization using link analysis based on rhetorical relations between sentences

With the accelerating rate of data growth on the Internet, automatic multi-document summarization has become an important task. In this paper, we propose a link analysis incorporated with rhetorical relations between sentences to perform extractive summarization for multiple-documents. We make use of the documents headlines to extract sentences with salient terms from the documents set using statistical model. Then we assign rhetorical relations learned by SVMs to determine the connectivity between the sentences which include the salient terms. Finally, we rank these sentences by measuring their relative importance within the document set based on link analysis method, PageRank. The rhetorical relations are used to evaluate the complementarity and redundancy of the ranked sentences. Our evaluation results show that the combination of PageRank along with rhetorical relations among sentences does help to improve the quality of extractive summarization.

Keyword extraction of radio news using term weighting with an encyclopedia and newspaper articles

In this paper, we propose a method for keyword extraction of radio news. Using our method, data sparseness problem and false alarm problem was Iightened even for short discourse or document. Also, our method is robust for partial errors of phoneme recognition. In our method, there are two procedures: i.e. term weighting and keyword extraction. In procedure of term weighting, a feature vector of each domain is calculated using an encyclopedia and newspaper articles. In procedure of keyword extraction, keywords are extracted using feature vectors and result of domain identification. The results of experiments demonstrate the appbcability of the method.

Automatic recognition of verbal polysemy

Polysemy is one of the major causes of difficulties in semantic clustering of words in a corpus. In this paper, we first give a definition of polysemy from the viewpoint of clustering and then, based on this definition, we propose a clustering method which recognises verbal polysemies from a textual corpus. The results of experiments demonstrate the effectiveness of the proposed method.

Correcting category errors in text classification

We address the problem dealing with category annotation errors which deteriorate the overall performance of text classification. We use two techniques. The first is support vectors which are extracted from the training samples by a machine learning technique, Support Vector Machines (SVM). The second is a loss function which measures the degree of our disappointment in any differences between the true distribution over inputs and the learners prediction. We apply it to the extracted support vectors, and correct annotation errors. Experimental results with the RWCP and the Reuters 1996 corpora show that our method achieves high precision in detecting and correcting annotation errors. Further, results on text classification improves accuracy.

Detecting shifts in news stories for paragraph extraction

For multi-document summarization where documents are collected over an extended period of time, the subject in a document changes over time. This paper focuses on subject shift and presents a method for extracting key paragraphs from documents that discuss the same event. Our extraction method uses the results of event tracking which starts from a few sample documents and finds all subsequent documents that discuss the same event. The method was tested on the TDT1 corpus, and the result shows the effectiveness of the method.

Extracting key paragraph based on topic and event detection: towards multi-document summarization

This paper proposes a method for extracting key paragraph for multi-document summarization based on distinction between a topic and an event. A topic and an event are identified using a simple criterion called domain dependency of words. The method was tested on the TDT1 corpus which has been developed by the TDT Pilot Study and the result can be regarded as promising the idea of domain dependency of words effectively employed.