1,723,279 research outputs found
Extracting Domain-Dependent Semantic Orientations of Latent Variables for Sentiment Classification
No full textSentiment analysis of weblogs is a challenging problem. Most previous work utilized semantic orientations of words or phrases to classify sentiments of weblogs. The problem with this approach is that semantic orientations of words or phrases are investigated without considering the domain of weblogs. Weblogs contain the author's various opinions about multifaceted topics. Therefore, we have to treat a semantic orientation domain-dependently. In this paper, we present an unsupervised learning model based on aspect model to classify sentiments of weblogs. Our model utilizes domain-dependent semantic orientations of latent variables instead of words or phrases, and uses them to classify sentiments of weblogs. Experiments on several domains confirm that our model assigns domain-dependent semantic orientations to latent variables correctly, and classifies sentiments of weblogs effectively.111Nsciescopu
GENETIC AND SEROLOGIC CLASSIFICATION OF SWINE INFLUENZA VIRUS H3N2 ISOLATES
No full textLee, J. H.; Choi, Y.K.; Joo, Han. (2003). GENETIC AND SEROLOGIC CLASSIFICATION OF SWINE INFLUENZA VIRUS H3N2 ISOLATES. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/160020
New services competition and positioning in the Korean telecommunications market
No full textVarious new services have been studied, and numerous other promising new functions and value-added services will be developed in the Korean telecommunications market. However, the continuous appearance of new services and the increasing competition are making it difficult to predict whether any service will ultimately achieve commercial success or how it should be positioned in the telecommunications service market. This article studies the characteristics and main features of the new communications services from the perspective of market environment change and competition, and discusses how they should be positioned in the telecommunications market
Method of Mining Subtopics Using Dependency Structure and Anchor Texts
No full textThis paper proposes a method that mines subtopics using the co-occurrence of words based on the dependency structure, and anchor texts from web documents in Japanese. We extracted subtopics using simple patterns which reflected the dependency structure, and evaluated subtopics by the proposed score equation. Our method achieved good performance than previous methods which used related or suggested queries from major web search engines. The results of our method will be useful in various search scenarios, such as query suggestion and result diversification.11Nsciescopu
Subtopic Mining Based on Head-Modifier Relation and Co-occurrence of Intents Using Web Documents
No full textThis paper proposes a method that mines subtopics using the head-modifier relation and co-occurrence of users' intents from web documents in Japanese. We extracted subtopics using the simple patterns based on the head-modifier relation between the query and its adjacent words, and returned the ranked list of subtopics by the proposed score equation. We re-ranked subtopics according to the intent co-occurrence measure. Our method achieved good performance than the baseline methods and suggested queries from the major web search engine. The results of our method will be useful in various search scenarios, such as query suggestion and result diversification.1111Nsciescopu
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
STRUCTURAL RE-RANKING WITH CLUSTER-BASED RETRIEVAL
No full textRe-ranking (RR) and Cluster-based Retrieval (CR) have been polar methods for improving retrieval effectiveness by using inter-document similarities. However, RR and CR improve precision and recall respectively, not simultaneously. Thus, the improvement through RR and CR may be different according to whether a query is recall-deficient or not. However, previous researchers missed out this point, and separately investigated individual approaches, causing a limited improvement. To reflect all of positive effects by RR and CR, this paper proposes RCR, the re-ranking with cluster-based retrieval where RR is applied to initially-retrieved results of CR. Experimental results show that RCR significantly improves the baseline, while CR or RR sometimes does not significantly improve the baseline.1122Nsciescopu
QUERY-BASED INTER-DOCUMENT SIMILARITY USING PROBABILISTIC CO-RELEVANCE MODEL
No full textInter-document similarity is the critical information which determines whether or not the cluster-based retrieval improves the baseline. However, a theoretical work on inter-document similarity has not been investigated, even though such work can provide a principle to define a more improved similarity in a well-motivated direction. To support this theory, this paper starts from pursuing an ideal inter-document similarity that optimally satisfies the cluster-hypothesis. We propose a probabilistic principle of inter-document similarities; the optimal similarity of two documents should be proportional to the probability that they are co-relevant to an arbitrary query. Based on this principle, the study of the inter-document similarity is formulated to attack the estimation problem of the co-relevance model of documents. Furthermore, we obtain that the optimal inter-document similarity should be defined using queries as its basic unit, not terms, namely a query-based similarity. We strictly derive a novel query-based similarity from the co-relevance model, without any heuristics. Experimental results show that the new query-based inter-document similarity significantly improves the previously-used term-based similarity in the context of Voorhee's evaluation measure.110Nsciescopu
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