Výsledky vyhledávání

Mining and Visualizing Online Web Content Using BAM: Brand Association Map

Autor: Navot Akiva, Eliyahu Greitzer, Yakir Krichman, Jonathan Schler

Publikováno v: Proceedings of the International AAAI Conference on Web and Social Media. 2:170-171

In this paper, we describe our Brand Association Map (BAM) tool which maps and visualizes the way consumers naturally think and talk about brands across billions of unaided conversations online. BAM is a semi-supervised tool that leverages text-minin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::649a1afafcd7d41f8085863ec28040f7
https://doi.org/10.1609/icwsm.v2i1.18635

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Computerized Source Criticism of Biblical Texts

Autor: Nachum Dershowitz, Navot Akiva, Moshe Koppel, Idan Dershowitz

Publikováno v: ResearcherID

We have developed an automated method to separate biblical texts according to author or scribal school. At the core of this new approach is the identification of correlations in word preference that are then used to quantify stylistic similarity betw

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0f65a7c7d858fb08b263a2c4240705c
https://doi.org/10.1353/jbl.2015.0016

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A generic unsupervised method for decomposing multi-author documents

Autor: Navot Akiva, Moshe Koppel

Publikováno v: Journal of the American Society for Information Science and Technology. 64:2256-2264

Given an unsegmented multi-author text, we wish to automatically separate out distinct authorial threads. We present a novel, entirely unsupervised, method that achieves strong results on multiple testbeds, including those for which authorial threads

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b39b06b3419ca0d9fcf72df30a2f85ec
https://doi.org/10.1002/asi.22924

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A Hybrid Approach of Classifier and Clustering for Solving the Missing Node Problem

Autor: Sigal Sina, Avi Rosenfeld, Sarit Kraus, Navot Akiva

Publikováno v: Proceedings of the AAAI Conference on Artificial Intelligence. 29

An important area of social network research is identifying missing information which is not explicitly represented in the network or is not visible to all. In this paper, we propose a novel Hybrid Approach of Classifier and Clustering,a which we ref

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9870476a005fd0112a606f133da77829
https://doi.org/10.1609/aaai.v29i1.9185

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Feature instability as a criterion for selecting potential style markers

Autor: Ido Dagan, Navot Akiva, Moshe Koppel

Publikováno v: Journal of the American Society for Information Science and Technology. 57:1519-1525

We introduce a new measure on linguistic features, called stability, which captures the extent to which a language element such as a word or a syntactic construct is replaceable by semantically equivalent elements. This measure may be perceived as qu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5a18053f69884cdcfd8ace581cd467b6
https://doi.org/10.1002/asi.20428

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A Unified View on Data Path Aspects for Sensing Applications at a Smart City Scale

Autor: M. Walther, Vangelis Gazis, Navot Akiva, Martin Strohbach

Publikováno v: AINA Workshops

Founded upon the Internet of Things (IoT), the technological landscape of Smart Cities brings a wide range of operational parameters which pose significant challenges for architectural concerns. Herein we provide a unified view on the data path issue

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4255c8617ad9ce0f37a60cbc38652d8a
https://doi.org/10.1109/waina.2013.66

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Identifying Distinct Components of a Multi-author Document

Autor: Navot Akiva, Moshe Koppel

Publikováno v: EISIC

Given a multi-author document, we use unsupervised methods to identify distinct authorial threads. Although this problem is of great practical interest for security and forensic reasons, as well as for commercial purposes, this paper is, to the best

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a7df4b099adf7f910b34ee3b860592c8
https://doi.org/10.1109/eisic.2012.16

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Automatically Classifying Documents by Ideological and Organizational Affiliation

Autor: Eli Alshech, Moshe Koppel, Kfir Bar, Navot Akiva

Publikováno v: ISI

We show how an Arabic language religious-political document can be automatically classified according to the ideological stream and organizational affiliation that it represents. Tests show that our methods achieve near-perfect accuracy.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::66da009722f4fe4e319861d055477da4
https://doi.org/10.1109/isi.2009.5137294

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Efficient unsupervised recursive word segmentation using minimum description length

Autor: Amihood Amir, Oren Kapah, Navot Akiva, Shlomo Argamon

Publikováno v: COLING

Automatic word segmentation is a basic requirement for unsupervised learning in morphological analysis. In this paper, we formulate a novel recursive method for minimum description length (MDL) word segmentation, whose basic operation is resegmenting

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d445c06eae1a2bde142b0ae6802bf1b9
https://doi.org/10.3115/1220355.1220507

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