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Keyword search is a user-friendly way to query XML data, such that users do not need to understand the complex syntax of structured query languages and the complex structural information of the underlying XML data. However, existing semantics suffer from limited expressiveness, thus users cannot obtain desired
of text summarization is accurate identification of keywords from the given textual content. In this paper, the relative performance of three popular algorithms, namely TextRank, LexRank and Latent Semantic Analysis for keyword extraction were investigated by measuring their effectiveness in identifying keywords from
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
This paper proposes a new keyword extraction method that uses bag-of-concept to extract keywords from Arabic text. The proposed algorithm utilizes semantic vector space model instead of traditional vector space model to group words into classes. The new method built word-context matrix where the synonym words will be
efficiently providing quality query suggestions for keyword queries on an XML document. We illustrate certain biases in previous work and propose a principled and general framework, XClean, based on the state-of-the-art language model. Compared with previous methods, XClean can accommodate different error models and XML keyword
Graph keyword search has drawn many research interests, since graph models can generally represent both structured and unstructured databases and keyword searches can extract valuable information for users without the knowledge of the underlying schema and query language. In practice, data graphs can be extremely
Audio mining is a speaker independent speech processing technique and is related to data mining. Keyword spotting plays an important role in audio mining. Keyword spotting is retrieval of all instances of a given keyword in spoken utterances. It is well suited to data mining tasks that process large amount of speech
relational database of web pages. So there are many researches focusing on the search in these relational database with keywords, compared with these researches, our algorithms are mainly based on bags using the greedy algorithms and supporting the phrase recognition by utilizing multiple dictionaries. We make a comparison
In this paper, we present a novel language-independent algorithm for extracting text-lines from handwritten document images. Our algorithm is based on the seam carving approach for content aware image resizing. We adopted the signed distance transform to generate the energy map, where extreme points indicate the
Classical algorithms of keywords extraction can hardly get low computational complexity and high accuracy. The association rule mining based algorithm is proposed in this paper. This algorithm adopts improved FP-Growth algorithm to extract word co-occurrence information, utilizes the similarity algorithm to eliminate
This paper proposes a new methodology that automatically generates English mnemonic keywords to support the learning of basic Japanese vocabulary. A new phonetic algorithm, called JemSoundex, is also introduced for phonetically transliterating the Japanese and English languages for phonetic matching. The effective
, which use Chinese automatic word segmentation technology, firstly extracts keywords by counting the word frequency and then removes useless words from word frequency table and obtains the hot topic through the keywords positive connection. We described the method in details with a experiment which can prove the validity
In order to over the shortcoming of the incomprehensive of summarization, a new lexical-chain-based keywords extraction and automatic summarization algorithm from Chinese texts based on the unknown word recognition using co-occurrence of neighbor words is proposed in this paper, and an algorithm for constructing
Keyword search over databases, popularized by keyword search in WWW, allows ordinary users to access database information without the knowledge of structured query languages and database schemas. Most of the previous studies in this area use IR-style ranking, which fail to consider the importance of the query answers
With the XML becomes a de-facto standard for exchanging and presenting information, the study on XML keyword search has become the focus of information retrieval. Several recent studies have finished the effective XML keyword search, but not all approach is effective in identifying return information, not all search
evaluate the collection of words and phrases to select set of keywords of the text. Next use the normal search engine to search the keywords set. Part of the search result will be used as seed links in focused crawler. Focused crawler's crawling policy is the best-first search policy, and this policy uses the similarity
communication channels of UAVs. Firstly, we capture the binary streams and segment into frames. And then we find frequent sequences utilizing multi-pattern matching algorithms. After that, we extract keywords based on frequency and analyze the association rules among these key words. Our experimental results show that 8-bit
segmentation which caused by unknown words have been affected the performance of Chinese keywords extraction, particularly in the field of technological text. In order to solve the problem, this paper proposes an improved method of keywords extraction based on the relationship among words. Experiments show that the proposed
multilingual information where backend will be English database and front-end uses local languages like Hindi, Marathi or Gujrathi. Our system provides an interface to enter a keyword in local language, the keyword will be parsed, query will be formed and display the result in local language. We had developed an efficient
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
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