Search (3 results, page 1 of 1)

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Abstract

Sentence ranking is the issue of most concern in document summarization today. While traditional feature-based approaches evaluate sentence significance and rank the sentences relying on the features that are particularly designed to characterize the different aspects of the individual sentences, the newly emerging graph-based ranking algorithms (such as the PageRank-like algorithms) recursively compute sentence significance using the global information in a text graph that links sentences together. In general, the existing PageRank-like algorithms can model well the phenomena that a sentence is important if it is linked by many other important sentences. Or they are capable of modeling the mutual reinforcement among the sentences in the text graph. However, when dealing with multidocument summarization these algorithms often assemble a set of documents into one large file. The document dimension is totally ignored. In this article we present a framework to model the two-level mutual reinforcement among sentences as well as documents. Under this framework we design and develop a novel ranking algorithm such that the document reinforcement is taken into account in the process of sentence ranking. The convergence issue is examined. We also explore an interesting and important property of the proposed algorithm. When evaluated on the DUC 2005 and 2006 query-oriented multidocument summarization datasets, significant results are achieved.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2119-2131

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Abstract

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

Footnote

Beitrag in einem Themenheft "Emotion and sentiment in social and expressive media"

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Abstract

Many scientific publications are now available on the World Wide Web for researchers to share research findings. However, they tend to be poorly organised, making the search of relevant publications difficult and time-consuming. Most existing search engines are ineffective in searching these publications, as they do not index Web publications that normally appear in PDF (portable document format) or PostScript formats. Proposes a Web citation-based retrieval system, known as PubSearch, for the retrieval of Web publications. PubSearch indexes Web publications based on citation indices and stores them into a Web Citation Database. The Web Citation Database is then mined to support publication retrieval. Apart from supporting the traditional cited reference search, PubSearch also provides document clustering search and author clustering search. Document clustering groups related publications into clusters, while author clustering categorizes authors into different research areas based on author co-citation analysis.