Topic
Ranking (information retrieval)
About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.
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22 Jul 2013TL;DR: A new metaphor of two-dimensional text for data-driven semantic modeling of natural language is proposed, which provides an entirely new angle on the representation of text: not only syntagmatic relations are annotated in the text, but also paradigmatic relations are made explicit by generating lexical expansions.
Abstract: A new metaphor of two-dimensional text for data-driven semantic modeling of natural language is proposed, which provides an entirely new angle on the representation of text: not only syntagmatic relations are annotated in the text, but also paradigmatic relations are made explicit by generating lexical expansions We operationalize distributional similarity in a general framework for large corpora, and describe a new method to generate similar terms in context Our evaluation shows that distributional similarity is able to produce highquality lexical resources in an unsupervised and knowledge-free way, and that our highly scalable similarity measure yields better scores in a WordNet-based evaluation than previous measures for very large corpora Evaluating on a lexical substitution task, we find that our contextualization method improves over a non-contextualized baseline across all parts of speech, and we show how the metaphor can be applied successfully to part-of-speech tagging A number of ways to extend and improve the contextualization method within our framework are discussed As opposed to comparable approaches, our framework defines a model of lexical expansions in context that can generate the expansions as opposed to ranking a given list, and thus does not require existing lexical-semantic resources
146 citations
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TL;DR: The results of two case studies show that the combined ranking of application descriptions and API documents yields the most-relevant search results from Exemplar.
Abstract: A fundamental problem of finding software applications that are highly relevant to development tasks is the mismatch between the high-level intent reflected in the descriptions of these tasks and low-level implementation details of applications. To reduce this mismatch we created an approach called EXEcutable exaMPLes ARchive (Exemplar) for finding highly relevant software projects from large archives of applications. After a programmer enters a natural-language query that contains high-level concepts (e.g., MIME, datasets), Exemplar retrieves applications that implement these concepts. Exemplar ranks applications in three ways. First, we consider the descriptions of applications. Second, we examine the Application Programming Interface (API) calls used by applications. Third, we analyze the dataflow among those API calls. We performed two case studies (with professional and student developers) to evaluate how these three rankings contribute to the quality of the search results from Exemplar. The results of our studies show that the combined ranking of application descriptions and API documents yields the most-relevant search results. We released Exemplar and our case study data to the public.
146 citations
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01 Sep 2013TL;DR: The task is outlined, the overall ranking of the submitted systems is presented, and the improvements to the state-of-the-art in keyphrase extraction are discussed.
Abstract: This paper describes the organization and results of the automatic keyphrase extraction task held at the Workshop on Semantic Evaluation 2010 (SemEval-2010). The keyphrase extraction task was specifically geared towards scientific articles. Systems were automatically evaluated by matching their extracted keyphrases against those assigned by the authors as well as the readers to the same documents. We outline the task, present the overall ranking of the submitted systems, and discuss the improvements to the state-of-the-art in keyphrase extraction.
146 citations
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30 Aug 2009TL;DR: This paper presents the first automated unsupervised analysis of LDA models to identify junk topics from legitimate ones, and to rank the topic significance.
Abstract: Topic models, like Latent Dirichlet Allocation (LDA), have been recently used to automatically generate text corpora topics, and to subdivide the corpus words among those topics. However, not all the estimated topics are of equal importance or correspond to genuine themes of the domain. Some of the topics can be a collection of irrelevant words, or represent insignificant themes. Current approaches to topic modeling perform manual examination to find meaningful topics. This paper presents the first automated unsupervised analysis of LDA models to identify junk topics from legitimate ones, and to rank the topic significance. Basically, the distance between a topic distribution and three definitions of "junk distribution" is computed using a variety of measures, from which an expressive figure of the topic significance is implemented using 4-phase Weighted Combination approach. Our experiments on synthetic and benchmark datasets show the effectiveness of the proposed approach in ranking the topic significance.
146 citations
01 Jan 2009
TL;DR: The first steps to reverse-engineering Google Scholar’s ranking algorithm are performed and the results may help authors to optimize their articles for Google Scholar and enable researchers to estimate the usefulness of Google Scholar with respect to their search intention and hence the need to use further academic search engines or databases.
Abstract: Google Scholar is one of the major academic search engines but its ranking algorithm for academic articles is unknown. We performed the first steps to reverse-engineering Google Scholar’s ranking algorithm and present the results in this research-in-progress paper. The results are: Citation counts is the highest weighed factor in Google Scholar’s ranking algorithm. Therefore, highly cited articles are found significantly more often in higher positions than articles that have been cited less often. As a consequence, Google Scholar seems to be more suitable for finding standard literature than gems or articles by authors advancing a new or different view from the mainstream. However, interesting exceptions for some search queries occurred. Moreover, the occurrence of a search term in an article’s title seems to have a strong impact on the article’s ranking. The impact of search term frequencies in an article’s full text is weak. That means it makes no difference in an article’s ranking if the article contains the query terms only once or multiple times. It was further researched whether the name of an author or journal has an impact on the ranking and whether differences exist between the ranking algorithms of different search modes that Google Scholar offers. The answer in both of these cases was "yes". The results of our research may help authors to optimize their articles for Google Scholar and enable researchers to estimate the usefulness of Google Scholar with respect to their search intention and hence the need to use further academic search engines or databases. Academic Search Engines, Google Scholar, Ranking Algorithm, Research in Progress
146 citations