Clustering Documents Using a Wikipedia-Based Concept Representation

Huang, Anna Y.Q.; Milne, David; Frank, Eibe; Witten, Ian H.

doi:10.1007/978-3-642-01307-2_62

Cited by 66 publications

(27 citation statements)

References 12 publications

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“…Huang, Milne, Frank, & Witten [10] mapped candidate phrases in the given document to Wikipedia articles by leveraging an informative and compact vocabulary -the collection of anchor texts in Wikipedia. The existing adopted method is more similar with Huang et al [10] used where Wikipedia's anchor text vocabulary is used to connect terms to Wikipedia articles.…”

Section: Related Workmentioning

confidence: 99%

“…The existing adopted method is more similar with Huang et al [10] used where Wikipedia's anchor text vocabulary is used to connect terms to Wikipedia articles. In this way the number of concepts in a document is no more than the number of terms.…”

Section: Related Workmentioning

confidence: 99%

“…Notice that although similarity is computed by considering all upper bounds for the two concept but the information measure has the effect of identifying minimal upper bounds, because no class is less informative than its superordinates. The semantic relatedness between term and its candidate concepts in a given document is computed according to the context information as follows (10).…”

Section: Fig 1 Overall Architecture Diagrammentioning

confidence: 99%

See 2 more Smart Citations

A novel semantic level text classification by combining NLP and Thesaurus concepts

Nagaraj¹,

Thiagarasu²

2014

IOSRJCE

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Fig 1 Overall Architecture Diagrammentioning

confidence: 99%

See 1 more Smart Citation

A novel semantic level text classification by combining NLP and Thesaurus concepts

Nagaraj¹,

Thiagarasu²

2014

IOSRJCE

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“…A second group of algorithms leverages the semantic representation to reduce data dimensionality [19]. Finally, other methods define new similarity measures that take into account the semantic relations between concepts [9,8,21]. However, operate solely in the semantic space is not always the best choice for document clustering: even though the same concept can be expressed by different terms, sometimes each term is specific to a particular domain or language register.…”

Section: Introductionmentioning

confidence: 99%

Concept-Enhanced Multi-view Co-clustering of Document Data

Rho

Pensa

2017

Lecture Notes in Computer Science

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Abstract. The maturity of structured knowledge bases and semantic resources has contributed to the enhancement of document clustering algorithms, that may take advantage of conceptual representations as an alternative for classic bag-of-words models. However, operating in the semantic space is not always the best choice in those domain where the choice of terms also matters. Moreover, users are usually required to provide a valid number of clusters as input, but this parameter is often hard to guess, due to the exploratory nature of the clustering process. To address these limitations, we propose a multi-view co-clustering approach that processes simultaneously the classic document-term matrix and an enhanced document-concept representation of the same collection of documents. Our algorithm has multiple key-features: it finds an arbitrary number of clusters and provides clusters of terms and concepts as easy-to-interpret summaries. We show the effectiveness of our approach in an extensive experimental study involving several corpora with different levels of complexity.

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“…Concept similarity based had been proposed for text classification [81] and text clustering [35,38].…”

Section: Short Text Clusteringmentioning

confidence: 99%

Event detection in social networks

Unankard¹

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Micro-blog services such as Twitter generate a large amount of messages carrying event information and users' opinions over a wide range of topics. The events discussed on social networks can be associated with topics, locations, and time periods. The events can be a variety, such as celebrities or political affairs, local social events, accidents, protests, or natural disasters. Messages are posted by users after they have experienced or witnessed the events happening in the real-world and they want to share their experiences immediately. People also express themselves spontaneously with respect to the social events in their social networks. Alternatively, policy-makers may want to know the feelings of users for a particular event to make informed decisions. With the increasing number of real-world events that are originated and discussed over social networks, event detection and tracking is becoming a compelling research issue. However, the traditional approaches to event detection and event tracking on large text streams are not applicable because of the following problems. First, they are not designed to deal with a large number of short and noisy messages. Second, social networks contain network structures such as friends, followers, replies, and re-tweets. Third, social network messages are associated with locations, which can be either senders' current locations or event locations. Fourth, each message is also associated with a timestamp. Messages often contain revealing and timely event information however, traditional text processing approaches assume documents are non-temporal. Moreover, given a particular time frame and a location the user is interested in, events that occurred in the given time frame from the chosen area are more valuable than others. Finding localized events has not been well studied yet.The goals of this thesis are to: (1) identify subsistent problems and challenges in event detection and tracking in streaming micro-blog text, (2) design approaches for event detection and event tracking in social networks, (3) design approaches for sentiment analysis for given event topics, and (4) evaluate the proposed approaches in real-world streaming datasets.In this thesis, our research is considered in three parts. Firstly, in order to detect emerging events from a large number of short and noisy messages, we propose an approach for the early detection of emerging hotspot events in social networks with location sensitivity. An algorithm is designed for slang conversion, synonym expansion and conceptual similarity to provide a rich semantic context for measuring message similarity to improve clustering results. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the hotspot emerging events. A sliding window manager is used to keep track of messages arriving in the system. The size of the sliding window is defined as the time interval. We evaluate our...

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Clustering Documents Using a Wikipedia-Based Concept Representation

Cited by 66 publications

References 12 publications

A novel semantic level text classification by combining NLP and Thesaurus concepts

A novel semantic level text classification by combining NLP and Thesaurus concepts

Concept-Enhanced Multi-view Co-clustering of Document Data

Event detection in social networks

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