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This paper proposes a lattice-based method for keyword spotting in online Chinese handwriting to improve the trade-off between accuracy and speed, and to overcome the out-of-vocabulary (OOV) problem of lexicon-driven approach. Using a character string recognition algorithm, the lattice-based method generates a
developed by implementing the keyword stripping using the Porter Stemmer algorithm. This could make the keyword search more efficient, as the root or stem word is only considered. Experimental results on two public spam corpuses are also discussed at the end.
Along with the rapid growth of the xml data quantity on the Internet, the xml data retrieval research has attracted more and more attention. The searching algorithm based on key words is a research hotspot in this field. We present a context-based layered intersection scan algorithm (CLISA), which uses the context semantic of key words to filter large amount of redundant information, different from...
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
order to eliminate noisy data when matching, the boundaries of candidates are relocated by the presented method named forward-backward keyword matching based on the corpus from People's Daily. Experimental results on Sogou corpus indicate that the trend selection method is better while compared to other template selection
only of keywords. Because source code has structure, approaches based on a structured retrieval model may yield improved performance. Indeed, Saha et al. Recently proposed a feature location technique based on structured retrieval that offers improved performance relative to a technique based on traditional TR. Although
As the popularity of text-based source code analysis grows, the use of stemmers to strip suffixes has increased. Stemmers have been used to more accurately determine relevance between a keyword query and methods in source code for search, exploration, and bug localization. In this paper, we investigate which
New applications and services aim to adapt themselves to the user's context and thus require platforms that can collect, distribute, and exchange contextual information. The Context-Aware Service Platform (CASP) can help, as exemplified here in three different use cases.
documents' keywords as nodes and the colocation of those keywords in a document as edges. We then exploit the particular nature of such graphs where co referent words are topologically clustered and can be efficiently discovered by our community detection algorithm. The accuracy of our technique is considerably higher than
public display raises specific challenges that may limit the applicability of existing recommender systems. In this paper, we explore the creation of a recommender system for public situated displays that is able to autonomously select relevant content from Internet sources using keywords as input. This type of recommender
of content. The main contribution of FIRSt is an integrated strategy that enables a content-based recommender to infer user interests by applying machine learning techniques, both on official item descriptions provided by a publisher and on freely keywords which users adopt to annotate relevant items. Static content and
searching. The distribution of tag types differs greatly across different systems. Also the distribution shows large difference between publishers and searchers. In order to expand tags of resources for publishers and keywords for searchers reasonable, this paper shows a comparison of the distributions of both kinds of users
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