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Keywords can be considered as condensed versions of documents, which can play important role in some text processing tasks such as text indexing, summarization and categorization. However, there are many digital documents especially on the Internet that do not have a list of assigned keywords. Assigning keywords to
Searching published papers is a required activity for the researching process. Since articles are presented in various languages, it makes precise queries hard to achieve. In this paper, we propose an automatic theses clustering method based on bilingual and synonymous keyword sets which includes Chinese and English
This work proposes an approach to address the problem of improving content selection in automatic text summarization by using probabilistic neural network (PNN). This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence
can be expected to be achieved in a QA system. Sentences are classified according to the content. Each classification is classified into a more detailed field. Important keywords are extracted from the sentences classified into the field. Moreover, the extracted keywords are classified into common and peculiar word for
This paper describes a new approach of enhancing textual document search and retrieval. The approach tries to take advantage of structured query languages in search and retrieval. For this purpose the semantic model of the document is created. The semantic model of the document is an ontology-like structured semantic annotation of the document that can support structured querying. This paper discusses...
by combining vectors of the named entities and keywords which can express the center vector of the topic more accurately. Then it deals with topic drift by single-pass clustering and continual modification of the topic center. The result of experiments shows that the new method can reduce the rate of missing and false
term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords
A large number of semistructured documents exist on the Web. We can find pages that contain keywords by using a search engine. But when we want to obtain information about an object like a notebook computer with 1 GB memory, a method is needed that automatically extracts attribute name (in this example
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.