147,328 research outputs found
Graph-Based Concept Clustering for Web Search Results
A search engine usually returns a long list of web search results corresponding to a query from the user. Users must spend a lot of time for browsing and navigating the search results for the relevant results. Many research works applied the text clustering techniques, called web search results clustering, to handle the problem. Unfortunately, search result document returned from search engine is a very short text. It is difficult to cluster related documents into the same group because a short document has low informative content. In this paper, we proposed a method to cluster the web search results with high clustering quality using graph-based clustering with concept which extract from the external knowledge source. The main idea is to expand the original search results with some related concept terms. We applied the Wikipedia as the external knowledge source for concept extraction. We compared the clustering results of our proposed method with two well-known search results clustering techniques, Suffix Tree Clustering and Lingo. The experimental results showed that our proposed method significantly outperforms over the well-known clustering techniques
A practical web-based approach to generating topic hierarchy for text segments
It is crucial in many information systems to organize short text segments, such as keywords in documents and queries from users, into a well-formed topic hierarchy. In this paper, we address the problem of generating topic hierarchies for diverse text segments with a general and practical approach that uses the Web as an additional knowledge source. Unlike long documents, short text segments typically do not contain enough information to extract reliable features. This work investigates the possibilities of using highly ranked search-result snippets to enrich the representation of text segments. A hierarchical clustering algorithm is then applied to create the hierarchical topic structure of text segments. Different from traditional clustering algorithms, which tend to pro-duce cluster hierarchies with a very unnatural shape, the approach tries to produce a more natural and comprehen-sive hierarchy. Extensive experiments were conducted on different domains of text segments. The obtained results have shown the potential of the proposed approach, which is believed able to benefit many information systems
Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
Measuring the similarity of short written contexts is a fundamental problem
in Natural Language Processing. This article provides a unifying framework by
which short context problems can be categorized both by their intended
application and proposed solution. The goal is to show that various problems
and methodologies that appear quite different on the surface are in fact very
closely related. The axes by which these categorizations are made include the
format of the contexts (headed versus headless), the way in which the contexts
are to be measured (first-order versus second-order similarity), and the
information used to represent the features in the contexts (micro versus macro
views). The unifying thread that binds together many short context applications
and methods is the fact that similarity decisions must be made between contexts
that share few (if any) words in common.Comment: 23 page
Dublin City University video track experiments for TREC 2003
In this paper, we describe our experiments for both the News Story Segmentation task and Interactive Search task for
TRECVID 2003. Our News Story Segmentation task involved the use of a Support Vector Machine (SVM) to combine evidence from audio-visual analysis tools in order to generate a listing of news stories from a given news programme. Our
Search task experiment compared a video retrieval system based on text, image and relevance feedback with a text-only
video retrieval system in order to identify which was more effective. In order to do so we developed two variations of our FĂschlĂĄr video retrieval system and conducted user testing in a controlled lab environment. In this paper we outline our work on both of these two tasks
TopSig: Topology Preserving Document Signatures
Performance comparisons between File Signatures and Inverted Files for text
retrieval have previously shown several significant shortcomings of file
signatures relative to inverted files. The inverted file approach underpins
most state-of-the-art search engine algorithms, such as Language and
Probabilistic models. It has been widely accepted that traditional file
signatures are inferior alternatives to inverted files. This paper describes
TopSig, a new approach to the construction of file signatures. Many advances in
semantic hashing and dimensionality reduction have been made in recent times,
but these were not so far linked to general purpose, signature file based,
search engines. This paper introduces a different signature file approach that
builds upon and extends these recent advances. We are able to demonstrate
significant improvements in the performance of signature file based indexing
and retrieval, performance that is comparable to that of state of the art
inverted file based systems, including Language models and BM25. These findings
suggest that file signatures offer a viable alternative to inverted files in
suitable settings and from the theoretical perspective it positions the file
signatures model in the class of Vector Space retrieval models.Comment: 12 pages, 8 figures, CIKM 201
Automatic document classification of biological literature
Background: Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature.
Results: We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578) compared to previously published results (F-value of 0.55 vs. 0.49), while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept.
Conclusions: We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept
Supporting aspect-based video browsing - analysis of a user study
In this paper, we present a novel video search interface based on the concept of aspect browsing. The proposed strategy is to assist the user in exploratory video search by actively suggesting new query terms and video shots. Our approach has the potential to narrow the "Semantic Gap" issue by allowing users to explore the data collection. First, we describe a clustering technique to identify potential aspects of a search. Then, we use the results to propose suggestions to the user to help them in their search task. Finally, we analyse this approach by exploiting the log files and the feedbacks of a user study
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