188,456 research outputs found
Special Libraries, February 1962
Volume 53, Issue 2https://scholarworks.sjsu.edu/sla_sl_1962/1001/thumbnail.jp
Multiple Retrieval Models and Regression Models for Prior Art Search
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend
Instrumentation
published or submitted for publicatio
Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy
Expert finding is an information retrieval task concerned with the search for
the most knowledgeable people, in some topic, with basis on documents
describing peoples activities. The task involves taking a user query as input
and returning a list of people sorted by their level of expertise regarding the
user query. This paper introduces a novel approach for combining multiple
estimators of expertise based on a multisensor data fusion framework together
with the Dempster-Shafer theory of evidence and Shannon's entropy. More
specifically, we defined three sensors which detect heterogeneous information
derived from the textual contents, from the graph structure of the citation
patterns for the community of experts, and from profile information about the
academic experts. Given the evidences collected, each sensor may define
different candidates as experts and consequently do not agree in a final
ranking decision. To deal with these conflicts, we applied the Dempster-Shafer
theory of evidence combined with Shannon's Entropy formula to fuse this
information and come up with a more accurate and reliable final ranking list.
Experiments made over two datasets of academic publications from the Computer
Science domain attest for the adequacy of the proposed approach over the
traditional state of the art approaches. We also made experiments against
representative supervised state of the art algorithms. Results revealed that
the proposed method achieved a similar performance when compared to these
supervised techniques, confirming the capabilities of the proposed framework
Special Libraries, June 1910
Volume 1, Issue 6https://scholarworks.sjsu.edu/sla_sl_1910/1005/thumbnail.jp
Special Libraries, January 1953
Volume 44, Issue 1https://scholarworks.sjsu.edu/sla_sl_1953/1000/thumbnail.jp
Special Libraries, July-August 1958
Volume 49, Issue 6https://scholarworks.sjsu.edu/sla_sl_1958/1005/thumbnail.jp
Learning to Extract Keyphrases from Text
Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97)
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