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A high-performance FAQ retrieval system uses query-log clustering to resolve lexical-disagreement problems. The proposed system outperforms traditional information-retrieval systems in FAQ retrieval.
Nowadays searching over structure P2P network mainly use literal matching words occurrence in documents with those present in user queries. But this process of searching can not deal with the semantics and semantic relationships of words. In this paper, we address these problems by proposing semantic search over
Eye movement patterns are the order in which keywords or sections of keywords are read. These patterns are an important component of how programmers read source code. One strategy for determining how programmers perform summarization tasks is through eye tracking studies. These studies examine where people focus their
their queries freely using natural language. The system processes the queries by extracting relevant keywords and discards those that do not carry much information and then returns the relevant sections of law which contain these keywords or keyphrases. The system also returns a list of relevant Supreme Court cases. The
This paper introduces a method of constructing a semantic dictionary automatically from the keywords and classify relations of the web encyclopedia Chinese WikiPedia. Semantic units, which are affixes (core/modifier) shared between many phrased-keywords, are selected using statistic method and string affix matching
The query method most commonly used is keywords query. The recall of document can be greatly improved through the expansion of synonymy, near-synonymy and hyponymy of keywords, but the precision may not always rise even to decline. In order to improve the precision, the mode of query expansion based on semantic
understanding of domain in which semantics of data is machine understandable. Second, we make in Raspberry Pi an interface which has the capability to recognize speech queries and give an oral response. Our interface analyzes each speech query, convert speech to text and extract keywords from the text. Later, these keywords are
While the problem to find needed information on the Web is being solved by the major search engines, access to the information in large text documents (e-books, conference proceedings, product manuals, etc) is still very rudimentary. Thus, keyword-search is often the only way to find the needle in the haystack. There
We examine the task of spoken term detection in Chinese spontaneous speech with a lattice-based approach. We first compare lattices generated with different units: word, character, tonal and toneless syllables, and also lattices converted from one unit to another unit. Then we combine lattices from multiple systems
be able to comprehend this type of knowledge, rather than merely relying on the valence of keywords and word co-occurrence frequencies. In this article, the largest existing taxonomy of common knowledge is blended with a natural-language-based semantic network of common-sense knowledge. Multidimensional scaling is
This paper presents a fast vocabulary-independent audio search method in Mandarin spontaneous speech which is based on syllable confusion network (SCN) indexing. Confusion network is linear and naturally suitable for indexing. The feasibility of using syllable confusion network as lattice representation is firstly
approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword. On a set of English movie review sentences, the best approach is to consider two words, following a negation keyword, to be negated by that keyword. This method yields a
, based on a semantic service-oriented approach. KnowleTracker has powerful deep mining functions to pull out news and other information that may lie several layers below the front page based on a semantic search for not only the specific keyword, but also the associated concepts that are not part of the keywords. The
Many language-oriented problems cannot be solved without semantic memory containing descriptions of concepts at different level of details. Automatic creation of semantic memories is a great challenge even for the simplest knowledge representation methods based on relations between concepts and keywords. Semantic
same concept. But the same type of classification is not successfully handled if it happens to be based on spatial keywords. This is due to the inherent ambiguity and uncertainty that is associated with the spatial terms found in natural language descriptions. Text documents imply the usage of natural language and as such
keywords of different languages are also revealed. We conducted experiments on a set of Chinese-English bilingual parallel corpora to discover the relationships between documents of these languages.
Keywords and searching template, the word segmentation algorithm based on the dictionary of keyword, the storage of searching template and the algorithm of template matching. On the foundation, we implement a QA system for Railway domain application, the experimental result show that QA system based on techniques we employed
index for the predicate, and then extracts the relevant knowledge and information of predicate keywords. Finally, a certain amount of complex natural language sentences are converted into more instances with teaching values, which provide basic resources for international Chinese teaching.
search in the program listings of TV stations. The goal is to find relevant program items, given a query formulated in natural language, or by using keywords. The queried data is a semantic knowledge base containing the data on the program listings as well as detailed information on distinct program items. We explore two
Document clustering addresses the problem of identifying groups of similar documents without human supervision. Unlike most existing solutions that perform document clustering based on keywords matching, we propose an algorithm that considers the meaning of the terms in the documents. For example, a document that
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