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A document surrogate is usually represented in a list of words. Because not all words in a document reflect its content, it is necessary to select important words from the document that relate to its content. Such important words are called keywords and are selected with a particular equation based on Term Frequency
The tool for keyword extraction developed within the AXMEDIS project have been designed for working in a multilingual environment and new algorithms have been developed to generate keywords with higher representativeness for content search and identification. The paper specifies the linguistic criteria followed for
data compression, the quick keyword index, and the automatic keyword selection, are discussed. These techniques, which are based on the statistical properties of word occurrence, are fairly simple, so that the information retrieval systems employing them can be implemented with ease. The data compression technique reduces
relevance weight between each query term and its relevant terms extracted from the snapshot of Google search result when that query term is used as search keyword. The estimated relevance weights are used to select good expansion terms for second retrieval. The experiments on the two test collections show that our query
classification/clustering as features. Also, this approach can be applied in keyword recommendation system in advertisement for different kinds of advertisers because of its expansibility and versatility.
proposed a formalized model of the text semantic similarity and similarity algorithm based on the case grammar. The semantic meanings of a sentence stem decide the similarity of a sentence. To the similarity sentence, a vector is used for the decorating case to get similarity algorithm. In this way, it avoided the keyword
A natural language information retrieval system ranks related documents according to criteria based on user query keywords and document similarities. However, many efforts have been made to make more useful query keywords because users do not use many keywords in their natural language search query when retrieving
A high-performance FAQ retrieval system uses query-log clustering to resolve lexical-disagreement problems. The proposed system outperforms traditional information-retrieval systems in FAQ retrieval.
To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed
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
, based on a semantic service-oriented approach. KnowleTracker has powerful deep mining functions to pull out news and other information that may lie several layers below the front page based on a semantic search for not only the specific keyword, but also the associated concepts that are not part of the keywords. The
relevant to a keyword-based query can be retrieved only if they share many words. Recently, Word Embeddings emerged and tried to cope with this problem by representing words in a language as vectors in a continuous vector space. An interesting property of these vectors is that two different words with similar meaning are
mapping Chinese question to SPARQL query with examples. At last, it implements the system and gives the retrieval result, which decreases the irrelevant searching return compared with the tradition information retrieval method based on keyword matching.
Clients' queries upon keywords or other informed description do not usually provide complete and unambiguous retrieval of information. Expansion of the queries based on semantic relation and phrase patterns is an effective approach to improve the retrieval. In this paper, a novel approach to queries expansion is
The query method most commonly used is keywords query. The recall of document can be greatly improved through the expansion of synonymy, near-synonymy and hyponymy of keywords, but the precision may not always rise even to decline. In order to improve the precision, the mode of query expansion based on semantic
same concept. But the same type of classification is not successfully handled if it happens to be based on spatial keywords. This is due to the inherent ambiguity and uncertainty that is associated with the spatial terms found in natural language descriptions. Text documents imply the usage of natural language and as such
search in the program listings of TV stations. The goal is to find relevant program items, given a query formulated in natural language, or by using keywords. The queried data is a semantic knowledge base containing the data on the program listings as well as detailed information on distinct program items. We explore two
a smaller, more informative domain ontology. In particular, we show that fully automated techniques based on keywords or topics have quite poor performance, while a semi-automated approach, requiring limited user involvement, can highly improve the filtering of domain concepts.
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