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This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to
In cross-language information retrieval (CLIR), the query sentence is often combined with a series of query keywords, rather than a complete natural sentence. Lack of necessary contextual syntactic information in such a query sentence makes it impossible to achieve a unique translation of the query sentence with
Question Understanding of Chinese Question-Answering System generally includes steps such as: word segmentation, POS Tagging, keywords expansion, information retrieval etc. The extended keyword set usually has redundant messages and part of the words and phrases may be not relevant to the question. Consequently
This paper proposes a system of retrieving English sentences by utilizing linguistically structural information. The userpsilas query consists of a sequence of keywords. The system automatically identifies dependency relations between occurrences of the keywords in sentences and classifies the sentences according to
The similarity between sentences is a theoretical basis and key technology to the question answering system. The method presented in this paper is as follows. Firstly, the dependency question sets are obtained and the key words are extracted from the major components of the question sentences and the target question form the related libraries, and then the candidate question sets are obtained through...
accuracy than individual classifiers. The maximum accuracy was got by enhancing the ensemble with an additional automatically generated domain specific class wise keyword list. Use of this system gave us greater than 4 percent improvement over the techniques of just using the ensemble classifier. A further improvement in
classification. It is grounded in a refined keyword-spotting method that employs: a WordNet-based word lexicon, a lexicon of emoticons, common abbreviations and colloquialisms, and a set of heuristic rules. The approach is implemented through the Synesketch software system. Synesketch is published as a free, open source software
Similarity computing of question is one of the important basis issues in the field of natural language processing, which was widely used in the Q&A system, and other areas such as question classification. In this paper, a method is presented that the keywords are extracted and then the similarity of keywords is
the websites into their most appropriate category. Several parameters like the weight applied to each feature and the keywords used to classify the websites were tuned to yield better results. The experimental evaluation revealed that the method implemented provides very high accuracy. In particularly, we obtained an
to describe a document instead of traditional keywords vector, which is based on merging words with high similarity and using a concept to describe the semantic feature rather than a series of words. It not only reduces feature dimension but also adds semantic information to the vector. We also use sentence (document
-demand into the sentence importance. The user-demand consists of the keywords that user queried. The experimental results show that this method can improve the accuracy of searching information.
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