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This paper proposes an unsupervised two-stage approach to automatically extract keywords from spoken documents. In the first stage, for each candidate term we compute a topic coherence and term significance measure (TCS) based on probabilistic latent semantic analysis (PLSA) models. In the second stage, we take the
We introduce a question-answering system that responds to a keywords-query by extracting information from linked data and generating reports in natural language (NL). Using entity disambiguation and distributed word similarity, we matched each keyword to a related entity and property in linked data. To extract keyword
The characteristic of poor information of short text often makes the effect of traditional keywords extraction not as good as expected. In this paper, we propose a graph-based ranking algorithm by exploiting Wikipedia as an external knowledge base for short text keywords extraction. To overcome the shortcoming of poor
popularity and co-occurrence data. We describe a prototype that leverages the Wikipedia category structure to allow a user to semantically navigate pages from the Delicious social bookmarking service. In our system a user can perform an ordinary keyword search and browse relevant pages but is also given the ability to broaden
Lack of overall ecological knowledge structure is a critical reason for learners' failure in keyword-based search. To address this issue, this paper firstly presents the dynamic location-aware and semantic hierarchy (DLASH) designed for the learners to browse images, which aims to identify learners' current
Search engines have become the main way for people to get expected information, most of them are based on keyword search. However, keyword search is based on computing the similarity of letters of the keywords, instead of semantic meaning, therefore the searching results often include irrelevant information to user
effort, and would most likely be disruptive. In this paper, we describe ways to use the results of semantic analysis and disambiguation, while retaining an existing keyword-based search and lexicographic index. We engineer this so the output of semantic analysis (performed off-line) is suitable for import directly into
Most researches on Image Retrieval (IR) have aimed at clearing away noisy images and allowing users to search only acceptable images for a target object specified by its object-name. We have become able to get enough acceptable images of a target object just by submitting its object-name to a conventional keyword
Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to
This study firstly notices that lack of overall ecologic knowledge structure is one critical reason for learners' failure of keyword search. Therefore in order to identify their current interesting sight, the dynamic location-aware and semantic hierarchy (DLASH) is presented for learners to browse images. This
This paper proposes an approach to finding answers within single text for a given question through extracting a network of categories from Wikipedia as background knowledge to support matching between question and answer. Experiments show that the approach is effective for keyword-based QA.
By using the market of data, we are able to make decision and solve problem for new business based on real-world data to do. When someone needs to find some suitable data employed to ideas that are going to be realized, we can use keywords derived from the ideas as query to search in the market of data. However
A method to automatically annotate video items with semantic metadata is presented. The method has been developed in the context of the Papyrus project to annotate documentary- like broadcast videos with a set of relevant keywords using automatic speech recognition (ASR) transcripts as a primary complementary resource
We study the problem of using Social Media to detect natural disasters, of which we are interested in a special kind, namely landslides. Employing information from Social Media presents unique research challenges, as there exists a considerable amount of noise due to multiple meanings of the search keywords, such as
their posts' keywords in each site, it bridges the gap between different vocabularies of different sites based on their semantic relatedness through concept-based interpretations, and it uses an efficient propagation algorithm to obtain the similarity between users from different sites, which can be used to construct the
with the language itself as well as due to lack of tools available to assist the researchers. In this paper, we are presenting a method to add semantically equivalent keywords in the questions by using semantic resources. The experiments suggest that the proposed research can deliver highly accurate answers for Arabic
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