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Two keyword-extraction ways are usually used, one is simply using the information from exactly single word like word frequency and TF.IDF, the other is based on the relationship between words. The relationship is usually described as word similarity which derives from a corpus (WordNet, HowNet) or man-made thesaurus
Keyword spotting is the task of identifying the occurrences of certain desired keywords in an arbitrary speech signal. Keyword spotting has many applications one of them is telephone routing. In particular, we consider a big company which receives thousands of telephone calls daily. We are interested with the
In this paper, a method of automatic Chinese keyword extraction based on KNN is proposed. Firstly, it preprocesses the document by vector space model. Secondly, it constructs a set of candidate keywords based on KNN method and the labeled dataset. Finally, it post-processes on candidate keywords by the character of
Handwritten word spotting aims at making document images amenable to browsing and searching by keyword retrieval. In this paper, we present a word spotting system based on Hidden Markov Models (HMM) that uses trained subword models to spot keywords. With the proposed method, arbitrary keywords can be spotted that do
using feature vector. We do static analysis over computed features to get distinguishing feature descriptors. Maximum similarity i.e. minimum distance allows us to find the query relevant combined pictures and associated relevant words. For textual part of the query we compute the concepts (keywords as well as synonyms of
explosion has became the main character of this age. Searching and making use of network information becomes more difficult. Therefore, automatically extraction on keyword is required. This paper uses the idea of classification to complete the task of Key-Phrase extraction, which uses SVM to build classification model and uses
This paper proposes a symbol feature-based hidden Markov model (HMM). Each state in the model is expressed by some symbol features, and is described by feature lists that draw from regular expressions and text inference; based on which, we use Viterbi Algorithm to extract the information from scientific researcherspsila homepages. It works well although there is great information redundancy.
Audio tags correspond to keywords that people use to describe different aspects of a music clip. With the explosive growth of digital music available on the Web, automatic audio tagging, which can be used to annotate unknown music or retrieve desirable music, is becoming increasingly important. This can be achieved by
learning approach. We use a graphical model, Dynamic Conditional Random Fields (DCRFs), for training our classifier. Our approach is based on semantic analysis of text to classify the predicates describing coexpression relationship rather than detecting the presence of keywords. We compared our results of sentence
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