Identifying variable-length meaningful phrases with correlation functions

Kim, H.; Chan, Philip K.

doi:10.1109/ictai.2004.70

Cited by 6 publications

(8 citation statements)

References 15 publications

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“…Our empirical results suggested that the AEMI correlation function and the MaxChildren threshold-finding method yielded more meaningful leaf clusters. In addition, by using phrases found by VPF algorithm [12], we improved performance up to 64% of interpretable clusters. We did not analyze differences among the UIHs' obtained from various users because of the large numbers of web pages used in our experiments.…”

Section: Fig 9 Uih With Words and Phrasesmentioning

confidence: 94%

“…To find phrases, we used the variable-length phrasefinding (VPF) algorithm [12] because it finds more meaningful phrases than other methods [1,4]. Phrases are used to enhance the representation of a UIH.…”

Section: Evaluation Data and Proceduresmentioning

confidence: 99%

“…We set a minimum number of terms (MinClusterSize) in a cluster as the stopping condition. In preparation for our clustering algorithm, we extract terms from web pages that are interesting to the user by filtering them through a stop list, stemming them [21,23], and adapting variable-length phrase-finding (VPF) algorithm [12]. Figure 3 illustrates the pseudo code for the DHC algorithm.…”

Section: Algorithmmentioning

confidence: 99%

“…The web pages are stemmed and filtered by ignoring the most common words listed in a stop list (called "function words") which are usually non-content words such as conjunctions, determiners, and prepositions [21,23]. The phrases are extracted by variable-length phrase-finding algorithm [12]. These processes are depicted in Fig.…”

Section: Problemmentioning

confidence: 99%

“…For instance, "apple" has different meanings in "apple tree" and in "apple computer". Therefore, we used phrases collected by a variable-length phrase-finding algorithm (VPF) [12].…”

Section: Introductionmentioning

confidence: 99%

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Learning implicit user interest hierarchy for context in personalization

Kim

Chan

2007

Appl Intell

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To provide a more robust context for personalization, we desire to extract a continuum of general to specific interests of a user, called a user interest hierarchy (UIH). The higher-level interests are more general, while the lower-level interests are more specific. A UIH can represent a user's interests at different abstraction levels and can be learned from the contents (words/phrases) in a set of web pages bookmarked by a user. We propose a divisive hierarchical clustering (DHC) algorithm to group terms (topics) into a hierarchy where more general interests are represented by a larger set of terms. Our approach does not need user involvement and learns the UIH "implicitly". To enrich features used in the UIH, we used phrases in addition to words. Our experiment indicates that DHC with the Augmented Expected Mutual Information (AEMI) correlation function and MaxChildren threshold-finding method built more meaningful UIHs than the other combinations on average; using words and phrases as features improved the quality of UIHs.

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Section: Fig 9 Uih With Words and Phrasesmentioning

confidence: 94%

Section: Evaluation Data and Proceduresmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Problemmentioning

confidence: 99%

“…For instance, "apple" has different meanings in "apple tree" and in "apple computer". Therefore, we used phrases collected by a variable-length phrase-finding algorithm (VPF) [12].…”

Section: Introductionmentioning

confidence: 99%

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Learning implicit user interest hierarchy for context in personalization

Kim

Chan

2007

Appl Intell

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Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks

Kim

Chan

2006

Advances in Web Mining and Web Usage Analysis

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Identifying well-formed biomedical phrases in MEDLINE® text

Kim

Yeganova

Comeau

et al. 2012

Journal of Biomedical Informatics

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In the modern world people frequently interact with retrieval systems to satisfy their information needs. Humanly understandable well-formed phrases represent a crucial interface between humans and the web, and the ability to index and search with such phrases is beneficial for human-web interactions. In this paper we consider the problem of identifying humanly understandable, well formed, and high quality biomedical phrases in MEDLINE documents. The main approaches used previously for detecting such phrases are syntactic, statistical, and a hybrid approach combining these two. In this paper we propose a supervised learning approach for identifying high quality phrases. First we obtain a set of known well-formed useful phrases from an existing source and label these phrases as positive. We then extract from MEDLINE a large set of multiword strings that do not contain stop words or punctuation. We believe this unlabeled set contains many well-formed phrases. Our goal is to identify these additional high quality phrases. We examine various feature combinations and several machine learning strategies designed to solve this problem. A proper choice of machine learning methods and features identifies in the large collection strings that are likely to be high quality phrases. We evaluate our approach by making human judgments on multiword strings extracted from MEDLINE using our methods. We find that over 85% of such extracted phrase candidates are humanly judged to be of high quality.

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Identifying variable-length meaningful phrases with correlation functions

Abstract: Finding meaningful phrases in a document

Cited by 6 publications

References 15 publications

Learning implicit user interest hierarchy for context in personalization

Learning implicit user interest hierarchy for context in personalization

Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks

Identifying well-formed biomedical phrases in MEDLINE® text

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