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This paper proposes a strategy of the summary sentence selection for query-focused multi-document summarization through extracting keywords from relevant document set. It calculates the query related feature and the topic related feature for every word in relevant document set, then obtains the importance of the word
In this paper we propose a novel and efficient technique for finding keywords typed by the user in digitised machine-printed historical documents using the dynamic time warping (DTW) algorithm. The method uses word portions located at the beginning and end of each segmented word of the processed documents and try to
This work demonstrates the development of Keyword Spotting (KWS) system using Vowel Onset Point (VOP), Vector Quantization (VQ) and Hidden Markov Model(HMM) based techniques. The goal of KWS system is to spot the keywords present in the test speech signal, while neglecting rest of the words. In this work, first
In previous work, we showed that using a lattice instead of the 1-best path to represent both the query and the utterance being searched is beneficial for spoken keyword spotting. In this paper, we introduce several techniques that further improve our multi-lattice alignment approach, including edit operation modeling
Keywords can be used to query XML data without schema information. In this paper, a novel kind of query is proposed, top-k keyword search over XML streams. According to the set of keywords and the number of results, such query can retrieve the top-k XML data fragments most related to the keyword set. A novel ranking
This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query. Given a database of spatial objects, each tuple is associated with some descriptive information represented in the form of keywords. The mCK query aims to find the spatially closest tuples which match m user-specified keywords
In this paper, we proposed a new 3D object retrieval method based on the visual keywords. In our method, the visual keywords are generated from the clusters of relative angle context distribution, which provides a statistical shape context that captures local shape characters and is also rotational and scale invariant
We consider topic detection without any prior knowledge of category structure or possible categories. Keywords are extracted and clustered based on different similarity measures using the induced k-bisecting clustering algorithm. Evaluation on Wikipedia articles shows that clusters of keywords correlate strongly with
We propose a feature word selection method for classifying recommended shops using Yelp customer reviews. TextRank keywords are extracted from the customer reviews to construct the sorted positive and negative keyword lists based on each keyword's summed TextRank scores. The top-K keywords are then aggregated
To alleviate the known semantic gap, it is necessary to integrate the two-modal parts of Web images, i.e. the low-level visual features and high-level semantic concepts (which are usually represented by keywords), for Web image retrieval. In this paper, we associate the keyword and visual features of Web images from a
In this work, we compare various text-based pornographic Web filtering techniques. The techniques include blacklist and keyword blocking. The technique called SV is modified to extract a representative feature vector. Each test Web pagepsilas feature is extracted and gathered as a vector. The vector is then summarized
String matching is a fundamental issue in computer science. This paper presents a lightweight string matching algorithm for short pattern matching, in which less than 20 keywords are often involved in the pattern set. The new algorithm makes use of condensed hash tables and computes the shift distance after each test
in a keyword-based photo retrieval process.We use metadata about the photo shot context (address location, nearby objects, season, light status...) to generate a bag of words for indexing each photo. We extend the Vector Space Model in order to transform these shot context words into document-vector terms. In addition
Ever growing music collections ask for novel ways of organization. The traditional browsing of folder hierarchies or search by title and album tends to be insufficient to maintain an overview of a collection of orders of thousands of tracks. Methods based on song similarity offer an alternative to keyword-based search
Content-based image retrieval (CBIR) has been adopted as a complementary technique to the keyword-based image search. Relevance feedback (RFB) is considered as an effective means to bridge the gap between the designated features and the run-time semantics on a CBIR system. Like many other interactive system, a good
Recently, the development of 3D model database systems and retrieval components are becoming increasingly important due to a rapidly growing amount of available 3D models. This has made the retrieval for specific 3D models become a vital issue. Unfortunately, traditional keyword searching techniques are not always
detail. The paper presents the similarity algorithm of domain keywords and common words respectively and integrates them into the question similarity. Experimental results show that the proposed method can achieve good performance and the system is applied.
In informal data sharing environments, misspellings cause problems for data indexing and retrieval. This is even more pronounced in mobile environments, in which devices with limited input devices are used. In a mobile environment, similarity search algorithms for finding misspelled data need to account for limited CPU and bandwidth. This demo shows P2P fast similarity search (P2PFastSS) running on...
In this paper, we will propose a domain ontology extensible method which can insert new keywords into the corresponding constructed domain ontology. The novel method uses TF-IDF (Term Frequency - Inverse Document Frequency) and LSA (Latent Semantic Analysis) to strengthen the semantic characteristic of keywords and
appear on websites with other text contents which can deliver important information about the image semantics. Popular image search engines use text contents surrounding the image to generate annotation keywords. Also emphasized text contents like headlines are assumed to be important description providers. Otherwise we
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