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commonly used are based on keywords, wherein provided keyword list doesn't consider the semantic relationship between keywords nor it consider the meaning of words and phrases. With such system, users frequently have problems expressing their information needs and translation those needs into requests. To overcome the
This paper proposes an emotion classification method for spoken utterances using a spoken-term detection (STD) method. This is a keyword extraction method using spoken utterances. The extracted keywords are used to decide on the emotion category of an utterance. Most keywords extracted by the STD system are redundant
Similarity analysis and keyword extraction are widely used as document relation analysis techniques. These methods are based on dictionary-base morphological analysis. However, they cannot meet the need when Internet grows fast and new words appear but dictionary can not be renewed fast enough. In this study, we
context information and semantic similarity together. We searched a series of context structures for keywords in a sentence. Experiment has been carried out to show the effectiveness of our method.
Term ambiguity — the challenge of having multiple potential meanings for a keyword or phrase — can be a major problem for search engines. Contextual information is essential for word sense disambiguation, but search queries are often limited to very few keywords, making the available textual context needed for
Cloud computing has emerged as a major way of reducing the information technology costs incurred by organizations. For a cost-sensitive organization which stores its data in the cloud and retrieves files via keyword search, the primary focus is on the issues of cost effectiveness as well as privacy. Three keyword
efficiency problems. Users preferences are almost ignored. So, the requirement of robust recommendation system is enhanced now a days. In this paper, we present review based service recommendation to dynamically recommend services to the users. Keywords are extracted from passive users reviews and a rating value is given to
As an SNS, Twitter is popular because users can post their emotions as a short message easily. Emotional tweets may influence user relationships. In our previous study, we found that positive users construct mutual relationships in Twitter. Keyword matching with emotional word dictionaries was used to detect positive
Keyword extraction problem is one of the most significant tasks in information retrieval. High-quality keyword extraction sufficiently influences the progress in the following subtasks of information retrieval: classification and clustering, data mining, knowledge extraction and representation, etc. The research
. In the area of searchable encryption, many works mainly focused on search criteria consisting of a single keyword or conjunctive keywords. Up until now, searching of the exact documents that contain a phrase, or consecutive keywords still remains an unsolved problem. We first define the model of phrase search over
in both Thai and English is built for helping users from a lot of keywords of the same term and (3) a set of keywords from herbal usages can be combined with the name keyword. From the results, information collected from KUIHerb is useful for searching.
problem, the data won't be leaked to the others. However, no operation can be performed if data are encrypted. To overcome such problem, a cryptographic protocol called SSE-1 was proposed where SSE-1 allows user to search with keyword over encrypted data. However, it is difficult to put SSE-1 to practical use since SSE-1
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
Twitter and social media as a whole has great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Current methods for disease surveillance on twitter rely on inflexible keyword based approaches that require
Nowadays the famous search engine companies are all providing the keyword web search capabilities. No one provides the high accurate & efficient user-requirements-oriented information Services. The task-focused massive multi-source heterogeneous information sharing & utilizing method and system is introduced
predefined keyword dictionary and then captures high- and low-frequency (rare) ADRs by information entropy. Extensive experiments on medical forums dataset demonstrate that CIE outperforms the state-of-the-art co-occurrence based methods, especially in rare ADRs detection.
to external hierarchical resource to polish accuracy of text matching. Also, a whole framework of text processing, keyword extraction and information matching is applied firstly among Chinese SMEs complementarity identification. By using machine learning algorithm, complementarities are digitalized and potential
Broadcasting companies have started offering video-on-demand services to provide their viewers with various kinds of TV programs. Most of these services have a program retrieval system that helps users find the TV programs they would like to watch. The conventional systems retrieve TV programs by keyword matching of
-efficiency and privacy. Three secure search protocols, namely Ostrovsky protocol, COPS protocol and EIRQ protocol are discussed in this paper. Each of these protocols incrementally improves upon the basic keyword search. Ostrovsky protocol offers privacy. COPS protocol aggregates the search results and achieves cost efficiency
This paper discusses the problem of sparse translation of English into Sundanese and Javanese that were found in Translator-Gator. Translator-Gator is a language game created by the United Nation Global Pulse, to support the research initiatives in Indonesia. Thousands of keyword were generated and translated from
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