TY - CHAP AU - Piskorski, Jakub AU - Yangarber, Roman A2 - Poibeau, Thierry A2 - Saggion, Horacio A2 - Piskorski, Jakub A2 - Yangarber, Roman T1 - Information Extraction: Past, Present and Future T2 - Multi-source, Multilingual Information Extraction and Summarization PB - Springer Berlin Heidelberg C1 - PY - 2013/ VL - IS - SP - 23 EP - 49 UR - http://dx.doi.org/10.1007/978-3-642-28569-1_2 DO - 10.1007/978-3-642-28569-1_2 KW - extraction KW - information KW - sota KW - survey L1 - SN - 978-3-642-28568-4 N1 - Information Extraction: Past, Present and Future - Springer N1 - AB - In this chapter we present a brief overview of Information Extraction, which is an area of natural language processing that deals with finding factual information in free text. In formal terms, ER - TY - GEN AU - A2 - Poibeau, Thierry A2 - Saggion, Horacio A2 - Piskorski, Jakub A2 - Yangarber, Roman T1 - Multi-source, multilingual information extraction and summarization JO - PB - Springer C1 - Berlin; New York PY - 2013/ VL - IS - SP - EP - UR - http://link.springer.com/book/10.1007/978-3-642-28569-1 DO - KW - extraction KW - information KW - multi KW - multilingual KW - sota KW - summarization L1 - N1 - Multi-source, Multilingual Information Extraction and Summarization - Springer N1 - AB - Information extraction (IE) and text summarization (TS) are powerful technologies for finding relevant pieces of information in text and presenting them to the user in condensed form. The ongoing information explosion makes IE and TS critical for successful functioning within the information society. These technologies face particular challenges due to the inherent multi-source nature of the information explosion. The technologies must now handle not isolated texts or individual narratives, but rather large-scale repositories and streams--in general, in multiple languages--containing a multiplicity of perspectives, opinions, or commentaries on particular topics, entities or events. There is thus a need to adapt existing techniques and develop new ones to deal with these challenges. This volume contains a selection of papers that present a variety of methodologies for content identification and extraction, as well as for content fusion and regeneration. The chapters cover various aspects of the challenges, depending on the nature of the information sought--names vs. events,-- and the nature of the sources--news streams vs. image captions vs. scientific research papers, etc. This volume aims to offer a broad and representative sample of studies from this very active research field. ER - TY - GEN AU - Bakshy, Eytan AU - Rosenn, Itamar AU - Marlow, Cameron AU - Adamic, Lada A2 - T1 - The Role of Social Networks in Information Diffusion JO - PB - C1 - PY - 2012/ VL - IS - SP - EP - UR - http://arxiv.org/abs/1201.4145 DO - KW - diffusion KW - facebook KW - information KW - toread L1 - N1 - The Role of Social Networks in Information Diffusion N1 - AB - Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these technologies on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed. ER - TY - CONF AU - Klügl, Peter AU - Toepfer, Martin AU - Lemmerich, Florian AU - Hotho, Andreas AU - Puppe, Frank A2 - Flach, Peter A. A2 - Bie, Tijl De A2 - Cristianini, Nello T1 - Collective Information Extraction with Context-Specific Consistencies. T2 - ECML/PKDD (1) PB - Springer C1 - PY - 2012/ CY - VL - 7523 IS - SP - 728 EP - 743 UR - http://dblp.uni-trier.de/db/conf/pkdd/pkdd2012-1.html#KluglTLHP12 DO - KW - 2012 KW - context KW - extraction KW - ie KW - information KW - myown L1 - SN - 978-3-642-33459-7 N1 - N1 - AB - ER - TY - CONF AU - Toepfer, Martin AU - Kluegl, Peter AU - Hotho, Andreas AU - Puppe., Frank A2 - Atzmüller, Martin A2 - Benz, Dominik A2 - Hotho, Andreas A2 - Stumme, Gerd T1 - Conditional Random Fields For Local Adaptive Reference Extraction T2 - Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet PB - C1 - Kassel, Germany PY - 2010/ CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/kdml21.pdf DO - KW - 2010 KW - crf KW - extraction KW - information KW - myown L1 - SN - N1 - N1 - AB - The accurate extraction of bibliographic information from scientific publications is an active field of research. Machine learning and sequence labeling approaches like Conditional Random Fields (CRF) are often applied for this reference extraction task, but still suffer from the ambiguity of reference notation. Reference sections apply a predefined style guide and contain only homogeneous references. Therefore, other references of the same paper or journal often provide evidence how the fields of a reference are correctly labeled. We propose a novel approach that exploits the similarities within a document. Our process model uses information of unlabeled documents directly during the extraction task in order to automatically adapt to the perceived style guide. This is implemented by changing the manifestation of the features for the applied CRF. The experimental results show considerable improvements compared to the common approach. We achieve an average F1 score of 96.7% and an instance accuracy of 85.4% on the test data set. ER - TY - BOOK AU - Manning, Christopher D. AU - Raghavan, Prabhakar AU - Schütze, Hinrich A2 - T1 - Introduction to Information Retrieval PB - Cambridge University Press C1 - PY - 2008/ VL - IS - SP - EP - UR - DO - KW - information KW - introduction KW - ir KW - retrieval KW - sota L1 - SN - N1 - Introduction to Information Retrieval N1 - AB - ER - TY - CONF AU - Sorg, Philipp AU - Cimiano, Philipp A2 - T1 - Cross-lingual Information Retrieval with Explicit Semantic Analysis T2 - Working Notes for the CLEF 2008 Workshop PB - C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://www.aifb.kit.edu/images/7/7c/2008_1837_Sorg_Cross-lingual_I_1.pdf DO - KW - cross KW - information KW - lingual KW - ol KW - ontology L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Hassan-Montero, Y. AU - Herrero-Solana, V. A2 - T1 - Improving Tag-Clouds as Visual Information Retrieval Interfaces T2 - InScit2006: International Conference on Multidisciplinary Information Sciences and Technologies PB - C1 - PY - 2006/ CY - VL - IS - SP - EP - UR - http://nosolousabilidad.com/hassan/improving_tagclouds.pdf DO - KW - clouds KW - dataset KW - del.icio.us KW - information KW - tag KW - tagging KW - taggingsurvey KW - toread KW - visual L1 - SN - N1 - N1 - AB - Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to re-finding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud�s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout. ER - TY - JOUR AU - Kayed, Mohammed AU - Shaalan, Khaled F. T1 - A Survey of Web Information Extraction Systems JO - IEEE Transactions on Knowledge and Data Engineering PY - 2006/ VL - 18 IS - 10 SP - 1411 EP - 1428 UR - DO - http://dx.doi.org/10.1109/TKDE.2006.152 KW - extraction KW - information KW - survey KW - ie L1 - SN - N1 - A Survey of Web Information Extraction Systems N1 - AB - ER - TY - CONF AU - Tang, Jie AU - Hong, MingCai AU - Li, Juan-Zi AU - Liang, Bangyong A2 - Cruz, Isabel F. A2 - Decker, Stefan A2 - Allemang, Dean A2 - Preist, Chris A2 - Schwabe, Daniel A2 - Mika, Peter A2 - Uschold, Michael A2 - Aroyo, Lora T1 - Tree-Structured Conditional Random Fields for Semantic Annotation. T2 - International Semantic Web Conference PB - Springer C1 - PY - 2006/ CY - VL - 4273 IS - SP - 640 EP - 653 UR - http://dblp.uni-trier.de/db/conf/semweb/iswc2006.html#TangHLL06 DO - KW - annotation KW - crf KW - extraction KW - information KW - ml KW - semantic L1 - SN - 3-540-49029-9 N1 - dblp N1 - AB - ER - TY - CONF AU - Culotta, A. AU - Bekkerman, R. AU - A.McCallum A2 - T1 - Extracting social networks and contact information

from email and the Web T2 - Proc. Conference on Email and Anti-Spam (CEAS) PB - C1 - Mountain View, USA PY - 2004/07 CY - VL - IS - SP - EP - UR - DO - KW - extraction KW - social KW - information KW - email KW - oe KW - networks L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Peng, Fuchun AU - McCallum, Andrew A2 - T1 - Accurate Information Extraction from Research Papers using Conditional Random Fields T2 - HLT-NAACL PB - C1 - PY - 2004/ CY - VL - IS - SP - 329 EP - 336 UR - http://www.cs.umass.edu/~mccallum/papers/hlt2004.pdf DO - KW - extraction KW - bibtex KW - information KW - ie KW - bibliographic KW - references L1 - SN - N1 - dblp N1 - AB - ER - TY - BOOK AU - Ferber, Reginald A2 - T1 - Information Retrieval: Suchmodelle und Data-Mining-Verfahren für Textsammlungen und das Web PB - dpunkt Verlag C1 - Heidelberg PY - 2003/ VL - IS - SP - EP - UR - http://information-retrieval.de/ DO - KW - information KW - ir KW - lecture KW - mining KW - retrieval KW - standard KW - vorlesung L1 - SN - N1 - N1 - AB - ER - TY - GEN AU - Newman, M. E. J. A2 - T1 - The structure and function of complex networks JO - PB - C1 - PY - 2003/03 VL - IS - SP - EP - UR - http://arxiv.org/abs/cond-mat/0303516 DO - KW - algorithm KW - clustering KW - complex_systems KW - folksonomy KW - information KW - kdubiq KW - network KW - retrieval KW - scale_free_networks KW - small KW - socialnetwork KW - summerschool KW - theory KW - web KW - web_graph KW - world L1 - N1 - N1 - AB - Inspired by empirical studies of networked systems such as the Internet,

social networks, and biological networks, researchers have in recent years

developed a variety of techniques and models to help us understand or predict

the behavior of these systems. Here we review developments in this field,

including such concepts as the small-world effect, degree distributions,

clustering, network correlations, random graph models, models of network growth

and preferential attachment, and dynamical processes taking place on networks. ER - TY - JOUR AU - Maedche, A. AU - Staab, S. AU - Studer, R. AU - Sure, Y. AU - Volz, R. T1 - SEAL -- Tying up Information Integration and Web Site

Management by Ontologies JO - IEEE-CS Data Engineering Bulletin, Special Issue on Organizingand Discovering the Semantic Web PY - 2002/03 VL - IS - SP - EP - UR - DO - KW - ontology KW - information KW - portal KW - integration L1 - SN - N1 - N1 - AB - ER - TY - BOOK AU - Baeza-Yates, Ricardo A. AU - Ribeiro-Neto, Berthier A. A2 - T1 - Modern Information Retrieval PB - ACM Press / Addison-Wesley C1 - PY - 1999/ VL - IS - SP - EP - UR - http://www.ischool.berkeley.edu/~hearst/irbook/glossary.html DO - KW - information KW - ir KW - lecture KW - retrieval KW - standard KW - vorlesung L1 - SN - 0-201-39829-X N1 - dblp N1 - AB - ER - TY - JOUR AU - Crestani, F. T1 - Application of Spreading Activation Techniques in Information Retrieval JO - Artificial Intelligence Review PY - 1997/12 VL - 11 IS - 6 SP - 453 EP - 482 UR - http://dx.doi.org/10.1023/A:1006569829653 DO - KW - *** KW - activation KW - information KW - ir KW - msn KW - network KW - retrieval KW - search KW - semantic KW - spreading KW - survey L1 - SN - N1 - SpringerLink - Zeitschriftenbeitrag N1 - AB - This paper surveys the use of Spreading Activation techniques onSemantic Networks in Associative Information Retrieval. The majorSpreading Activation models are presented and their applications toIR is surveyed. A number of works in this area are criticallyanalyzed in order to study the relevance of Spreading Activation forassociative IR.

ER - ER - TY - BOOK AU - A2 - Sparck-Jones, K. A2 - Willett, P. T1 - Readings in Information Retrieval PB - Morgan Kaufmann C1 - PY - 1997/ VL - IS - SP - EP - UR - DO - KW - retrieval KW - ir KW - information KW - readings L1 - SN - N1 - N1 - AB - ER - TY - BOOK AU - Sowa, J. F. A2 - T1 - Conceptual Structures: Information Processing in Mind and Machine PB - Addison-Wesley Publishing Company C1 - Reading, MA PY - 1984/ VL - IS - SP - EP - UR - DO - KW - processing KW - information KW - structures KW - machine KW - mind KW - conceptual L1 - SN - N1 - N1 - AB - ER - TY - BOOK AU - van Rijsbergen, C. J. A2 - T1 - Information retrieval PB - Butterworths C1 - London PY - 1979/ VL - IS - SP - EP - UR - http://www.dcs.gla.ac.uk/Keith/Preface.html DO - KW - advanced KW - information KW - ir KW - lecture KW - vorlesung L1 - SN - N1 - N1 - AB - ER -