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Academic literature on the topic 'Information Extraction'

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Published: 4 June 2021
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Journal articles on the topic "Information Extraction"

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Zdravcheva, Neli. "INFORMATION EXTRACTION FROM MULTISPECTRAL SATELLITE IMAGES." Journal Scientific and Applied Research 24, no. 1 (November 23, 2023): 25–31. http://dx.doi.org/10.46687/jsar.v24i1.364.

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The article analyzes various methods and approaches of modern remote sensing that can be used in the processing of multispectral satellite images in order to effectively extract visual information about territories for which preliminary data is not available. Attention is paid to the creation of new derivative images (synthesized and indexed) and to performing pixel-oriented computer non supervised classification. A series of experiments have been made that clearly reveal the advantages and conveniences of remote retrieval of information from multispectral satellite images in a territory for which reference objects and other data acquired in situ are not available.
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Sarawagi, Sunita. "Information Extraction." Foundations and Trends® in Databases 1, no. 3 (2007): 261–377. http://dx.doi.org/10.1561/1900000003.

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Cowie, Jim, and Wendy Lehnert. "Information extraction." Communications of the ACM 39, no. 1 (January 1996): 80–91. http://dx.doi.org/10.1145/234173.234209.

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McCallum, Andrew. "Information Extraction." Queue 3, no. 9 (November 2005): 48–57. http://dx.doi.org/10.1145/1105664.1105679.

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Grishman, Ralph. "Information Extraction." IEEE Intelligent Systems 30, no. 5 (September 2015): 8–15. http://dx.doi.org/10.1109/mis.2015.68.

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Kassaie, Besat, and Frank Wm Tompa. "Autonomously Computable Information Extraction." Proceedings of the VLDB Endowment 16, no. 10 (June 2023): 2431–43. http://dx.doi.org/10.14778/3603581.3603585.

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Most optimization techniques deployed in information extraction systems assume that source documents are static. Instead, extracted relations can be considered to be materialized views defined by a language built on regular expressions. Using this perspective, we can provide an efficient verifier (using static analysis) that can be used to avoid the high cost of re-extracting information after an update. In particular, we propose an efficient mechanism to identify updates for which we can autonomously compute an extracted relation. We present experimental results that support the feasibility and practicality of this mechanism in real world extraction systems.
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Ling, Xiao, and Daniel Weld. "Temporal Information Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1385–90. http://dx.doi.org/10.1609/aaai.v24i1.7512.

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Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1/20/93 — 1/20/01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible. In addition to recognizing temporal relations between times and events, TIE performs global inference, enforcing transitivity to bound the start and ending times for each event. We introduce the notion of temporal entropy as a way to evaluate the performance of temporal IE systems and present experiments showing that TIE outperforms three alternative approaches.
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Turmo, Jordi, Alicia Ageno, and Neus Català. "Adaptive information extraction." ACM Computing Surveys 38, no. 2 (July 25, 2006): 4. http://dx.doi.org/10.1145/1132956.1132957.

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Vo, Duc-Thuan, and Ebrahim Bagheri. "Open information extraction." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630003. http://dx.doi.org/10.1142/s2425038416300032.

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Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation types such as Verb, Noun and Prep, Verb and Prep, and Infinitive with deep linguistic analysis. They expose simple yet principled ways in which verbs express relationships in linguistics such as verb phrase-based extraction or clause-based extraction. They obtain a significantly higher performance over previous systems in the first generation. In this paper, we describe an overview of two Open IE generations including strengths, weaknesses and application areas.
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Aumann, Yonatan, Ronen Feldman, Yair Liberzon, Benjamin Rosenfeld, and Jonathan Schler. "Visual information extraction." Knowledge and Information Systems 10, no. 1 (April 4, 2006): 1–15. http://dx.doi.org/10.1007/s10115-006-0014-x.

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Dissertations / Theses on the topic "Information Extraction"

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Labský, Martin. "Information Extraction from Websites using Extraction Ontologies." Doctoral thesis, Vysoká škola ekonomická v Praze, 2002. http://www.nusl.cz/ntk/nusl-77102.

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Automatic information extraction (IE) from various types of text became very popular during the last decade. Owing to information overload, there are many practical applications that can utilize semantically labelled data extracted from textual sources like the Internet, emails, intranet documents and even conventional sources like newspaper and magazines. Applications of IE exist in many areas of computer science: information retrieval systems, question answering or website quality assessment. This work focuses on developing IE methods and tools that are particularly suited to extraction from semi-structured documents such as web pages and to situations where available training data is limited. The main contribution of this thesis is the proposed approach of extended extraction ontologies. It attempts to combine extraction evidence from three distinct sources: (1) manually specified extraction knowledge, (2) existing training data and (3) formatting regularities that are often present in online documents. The underlying hypothesis is that using extraction evidence of all three types by the extraction algorithm can help improve its extraction accuracy and robustness. The motivation for this work has been the lack of described methods and tools that would exploit these extraction evidence types at the same time. This thesis first describes a statistically trained approach to IE based on Hidden Markov Models which integrates with a picture classification algorithm in order to extract product offers from the Internet, including textual items as well as images. This approach is evaluated using a bicycle sale domain. Several methods of image classification using various feature sets are described and evaluated as well. These trained approaches are then integrated in the proposed novel approach of extended extraction ontologies, which builds on top of the work of Embley [21] by exploiting manual, trained and formatting types of extraction evidence at the same time. The intended benefit of using extraction ontologies is a quick development of a functional IE prototype, its smooth transition to deployed IE application and the possibility to leverage the use of each of the three extraction evidence types. Also, since extraction ontologies are typically developed by adapting suitable domain ontologies and the ontology remains in center of the extraction process, the work related to the conversion of extracted results back to a domain ontology or schema is minimized. The described approach is evaluated using several distinct real-world datasets.
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Arpteg, Anders. "Intelligent semi-structured information extraction : a user-driven approach to information extraction /." Linköping : Dept. of Computer and Information Science, Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/tek946s.pdf.

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Swampillai, Kumutha. "Information extraction across sentences." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575468.

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Most relation extraction systems identify relations by searching within- sentences (within-sentence relations). Such an approach excludes finding any relations that cross sentence boundaries (cross-sentence relations). This thesis quantifies the cross-sentence relations in two major information ex- traction corpora: ACE03 (9.4%) and MUC6 (27.4%), revealing the extent of this limitation. In response. a composite kernel approach to cross-sentence relation extraction is proposed which models relations using parse tree and fiat surface features. Support vector machine classifiers are trained using cross-sentential relations from the !vIUC6 corpus to determine the effective- ness of this approach. It was shown .that composite kernels are able to extract cross-sentential relations with f-measure scores of 0.512, 0.116 and 0.633 for PerOrg. PerPost and PostOrg models. respectively. Moreover. combining within-sentence and cross-sentence extraction models increases the number of relations correctly identified by 24% over within-sentence relation extraction alone.
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Tablan, Mihai Valentin. "Toward portable information extraction." Thesis, University of Sheffield, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522379.

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Leen, Gayle. "Context assisted information extraction." Thesis, University of the West of Scotland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446043.

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Sottovia, Paolo. "Information Extraction from data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242992.

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Data analysis is the process of inspecting, cleaning, extract, and modeling data with the intention of extracting useful information in order to support users in their decisions. With the advent of Big Data, data analysis was becoming more complicated due to the volume and variety of data. This process begins with the acquisition of the data and the selection of the data that is useful for the desiderata analysis. With such amount of data, also expert users are not able to inspect the data and understand if a dataset is suitable or not for their purposes. In this dissertation, we focus on five problems in the broad data analysis process to help users find insights from the data when they do not have enough knowledge about its data. First, we analyze the data description problem, where the user is looking for a description of the input dataset. We introduce data descriptions: a compact, readable and insightful formula of boolean predicates that represents a set of data records. Finding the best description for a dataset is computationally expensive and task-specific; we, therefore, introduce a set of metrics and heuristics for generating meaningful descriptions at an interactive performance. Secondly, we look at the problem of order dependency discovery, which discovers another kind of metadata that may help the user in the understanding of characteristics of a dataset. Our approach leverages the observation that discovering order dependencies can be guided by the discovery of a more specific form of dependencies called order compatibility dependencies. Thirdly, textual data encodes much hidden information. To allow this data to reach its full potential, there has been an increasing interest in extracting structural information from it. In this regard, we propose a novel approach for extracting events that are based on temporal co-reference among entities. We consider an event to be a set of entities that collectively experience relationships between them in a specific period of time. We developed a distributed strategy that is able to scale with the largest on-line encyclopedia available, Wikipedia. Then, we deal with the evolving nature of the data by focusing on the problem of finding synonymous attributes in evolving Wikipedia Infoboxes. Over time, several attributes have been used to indicate the same characteristic of an entity. This provides several issues when we are trying to analyze the content of different time periods. To solve it, we propose a clustering strategy that combines two contrasting distance metrics. We developed an approximate solution that we assess over 13 years of Wikipedia history by proving its flexibility and accuracy. Finally, we tackle the problem of identifying movements of attributes in evolving datasets. In an evolving environment, entities not only change their characteristics, but they sometimes exchange them over time. We proposed a strategy where we are able to discover those cases, and we also test our strategy on real datasets. We formally present the five problems that we validate both in terms of theoretical results and experimental evaluation, and we demonstrate that the proposed approaches efficiently scale with a large amount of data.
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Sottovia, Paolo. "Information Extraction from data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242992.

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Data analysis is the process of inspecting, cleaning, extract, and modeling data with the intention of extracting useful information in order to support users in their decisions. With the advent of Big Data, data analysis was becoming more complicated due to the volume and variety of data. This process begins with the acquisition of the data and the selection of the data that is useful for the desiderata analysis. With such amount of data, also expert users are not able to inspect the data and understand if a dataset is suitable or not for their purposes. In this dissertation, we focus on five problems in the broad data analysis process to help users find insights from the data when they do not have enough knowledge about its data. First, we analyze the data description problem, where the user is looking for a description of the input dataset. We introduce data descriptions: a compact, readable and insightful formula of boolean predicates that represents a set of data records. Finding the best description for a dataset is computationally expensive and task-specific; we, therefore, introduce a set of metrics and heuristics for generating meaningful descriptions at an interactive performance. Secondly, we look at the problem of order dependency discovery, which discovers another kind of metadata that may help the user in the understanding of characteristics of a dataset. Our approach leverages the observation that discovering order dependencies can be guided by the discovery of a more specific form of dependencies called order compatibility dependencies. Thirdly, textual data encodes much hidden information. To allow this data to reach its full potential, there has been an increasing interest in extracting structural information from it. In this regard, we propose a novel approach for extracting events that are based on temporal co-reference among entities. We consider an event to be a set of entities that collectively experience relationships between them in a specific period of time. We developed a distributed strategy that is able to scale with the largest on-line encyclopedia available, Wikipedia. Then, we deal with the evolving nature of the data by focusing on the problem of finding synonymous attributes in evolving Wikipedia Infoboxes. Over time, several attributes have been used to indicate the same characteristic of an entity. This provides several issues when we are trying to analyze the content of different time periods. To solve it, we propose a clustering strategy that combines two contrasting distance metrics. We developed an approximate solution that we assess over 13 years of Wikipedia history by proving its flexibility and accuracy. Finally, we tackle the problem of identifying movements of attributes in evolving datasets. In an evolving environment, entities not only change their characteristics, but they sometimes exchange them over time. We proposed a strategy where we are able to discover those cases, and we also test our strategy on real datasets. We formally present the five problems that we validate both in terms of theoretical results and experimental evaluation, and we demonstrate that the proposed approaches efficiently scale with a large amount of data.
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Arpteg, Anders. "Adaptive Semi-structured Information Extraction." Licentiate thesis, Linköping University, Linköping University, KPLAB - Knowledge Processing Lab, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5688.

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The number of domains and tasks where information extraction tools can be used needs to be increased. One way to reach this goal is to construct user-driven information extraction systems where novice users are able to adapt them to new domains and tasks. To accomplish this goal, the systems need to become more intelligent and able to learn to extract information without need of expert skills or time-consuming work from the user.

The type of information extraction system that is in focus for this thesis is semistructural information extraction. The term semi-structural refers to documents that not only contain natural language text but also additional structural information. The typical application is information extraction from World Wide Web hypertext documents. By making effective use of not only the link structure but also the structural information within each such document, user-driven extraction systems with high performance can be built.

The extraction process contains several steps where different types of techniques are used. Examples of such types of techniques are those that take advantage of structural, pure syntactic, linguistic, and semantic information. The first step that is in focus for this thesis is the navigation step that takes advantage of the structural information. It is only one part of a complete extraction system, but it is an important part. The use of reinforcement learning algorithms for the navigation step can make the adaptation of the system to new tasks and domains more user-driven. The advantage of using reinforcement learning techniques is that the extraction agent can efficiently learn from its own experience without need for intensive user interactions.

An agent-oriented system was designed to evaluate the approach suggested in this thesis. Initial experiments showed that the training of the navigation step and the approach of the system was promising. However, additional components need to be included in the system before it becomes a fully-fledged user-driven system.


Report code: LiU-Tek-Lic-2002:73.
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Schierle, Martin. "Language Engineering for Information Extraction." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-81757.

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Accompanied by the cultural development to an information society and knowledge economy and driven by the rapid growth of the World Wide Web and decreasing prices for technology and disk space, the world\'s knowledge is evolving fast, and humans are challenged with keeping up. Despite all efforts on data structuring, a large part of this human knowledge is still hidden behind the ambiguities and fuzziness of natural language. Especially domain language poses new challenges by having specific syntax, terminology and morphology. Companies willing to exploit the information contained in such corpora are often required to build specialized systems instead of being able to rely on off the shelf software libraries and data resources. The engineering of language processing systems is however cumbersome, and the creation of language resources, annotation of training data and composition of modules is often enough rather an art than a science. The scientific field of Language Engineering aims at providing reliable information, approaches and guidelines of how to design, implement, test and evaluate language processing systems. Language engineering architectures have been a subject of scientific work for the last two decades and aim at building universal systems of easily reusable components. Although current systems offer comprehensive features and rely on an architectural sound basis, there is still little documentation about how to actually build an information extraction application. Selection of modules, methods and resources for a distinct usecase requires a detailed understanding of state of the art technology, application demands and characteristics of the input text. The main assumption underlying this work is the thesis that a new application can only occasionally be created by reusing standard components from different repositories. This work recapitulates existing literature about language resources, processing resources and language engineering architectures to derive a theory about how to engineer a new system for information extraction from a (domain) corpus. This thesis was initiated by the Daimler AG to prepare and analyze unstructured information as a basis for corporate quality analysis. It is therefore concerned with language engineering in the area of Information Extraction, which targets the detection and extraction of specific facts from textual data. While other work in the field of information extraction is mainly concerned with the extraction of location or person names, this work deals with automotive components, failure symptoms, corrective measures and their relations in arbitrary arity. The ideas presented in this work will be applied, evaluated and demonstrated on a real world application dealing with quality analysis on automotive domain language. To achieve this goal, the underlying corpus is examined and scientifically characterized, algorithms are picked with respect to the derived requirements and evaluated where necessary. The system comprises language identification, tokenization, spelling correction, part of speech tagging, syntax parsing and a final relation extraction step. The extracted information is used as an input to data mining methods such as an early warning system and a graph based visualization for interactive root cause analysis. It is finally investigated how the unstructured data facilitates those quality analysis methods in comparison to structured data. The acceptance of these text based methods in the company\'s processes further proofs the usefulness of the created information extraction system.
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Lam, Man I. "Business information extraction from web." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1937939.

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Books on the topic "Information Extraction"

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Pazienza, Maria Teresa, ed. Information Extraction. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7.

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Maybury, Mark T., ed. Multimedia Information Extraction. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118219546.

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Paolo, Coletti, ed. Information extraction in finance. Southampton: WIT Press, 2008.

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Turenne, Nicolas, and Jean-Charles Pomerol, eds. Knowledge Needs and Information Extraction. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574560.

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Gonzalez, Pablo Javier Barrio. Ranking for Scalable Information Extraction. [New York, N.Y.?]: [publisher not identified], 2015.

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Pazienza, Maria Teresa, ed. Information Extraction in the Web Era. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11781.

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Cortez, Eli, and Altigran S. da Silva. Unsupervised Information Extraction by Text Segmentation. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02597-1.

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Verfasser, Piryani Rajesh, and Singh Vivek Kumar Verfasser, eds. Applied Information Extraction and Sentiment Analysis. Saarbrücken: LAP LAMBERT Academic Publishing, 2015.

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Teresa, Pazienza Maria, ed. Information extraction: Towards scalable, adaptable systems. Berlin: Springer-Verlag, 1999.

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Dahlke, Stephan, Wolfgang Dahmen, Michael Griebel, Wolfgang Hackbusch, Klaus Ritter, Reinhold Schneider, Christoph Schwab, and Harry Yserentant, eds. Extraction of Quantifiable Information from Complex Systems. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08159-5.

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Book chapters on the topic "Information Extraction"

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Vilain, Marc. "Inferential Information Extraction." In Information Extraction, 95–119. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_6.

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Wilks, Yorick, and Roberta Catizone. "Can We Make Information Extraction More Adaptive?" In Information Extraction, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_1.

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Chanod, Jean-Pierre. "Natural Language Processing and Digital Libraries." In Information Extraction, 17–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_2.

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Voorhees, Ellen M. "Natural Language Processing and Information Retrieval." In Information Extraction, 32–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_3.

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Dahl, Verónica. "From Speech to Knowledge." In Information Extraction, 49–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_4.

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Sowa, John F. "Relating Templates to Language and Logic." In Information Extraction, 76–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_5.

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Somers, Harold. "Knowledge Extraction from Bilingual Corpora." In Information Extraction, 120–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_7.

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Basili, Roberto, Massimo Di Nanni, and Maria Teresa Pazienza. "Engineering of IE Systems: An Object-Oriented Approach." In Information Extraction, 134–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_8.

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Nédellec, Claire, Adeline Nazarenko, and Robert Bossy. "Information Extraction." In Handbook on Ontologies, 663–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92673-3_30.

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Ji, Heng. "Information Extraction." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_204-2.

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Conference papers on the topic "Information Extraction"

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Badieh Habib Morgan, Mena, and Maurice van Keulen. "Information Extraction for Social Media." In Proceedings of the Third Workshop on Semantic Web and Information Extraction. Stroudsburg, PA, USA: Association for Computational Linguistics and Dublin City University, 2014. http://dx.doi.org/10.3115/v1/w14-6202.

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Okurowski, Mary Ellen. "Information extraction overview." In a workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1993. http://dx.doi.org/10.3115/1119149.1119164.

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Doan, AnHai, Raghu Ramakrishnan, and Shivakumar Vaithyanathan. "Managing information extraction." In the 2006 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1142473.1142595.

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Chiticariu, Laura, Yunyao Li, Sriram Raghavan, and Frederick R. Reiss. "Enterprise information extraction." In the 2010 international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807167.1807339.

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Fan, Siqi, Yequan Wang, Jing Li, Zheng Zhang, Shuo Shang, and Peng Han. "Interactive Information Extraction by Semantic Information Graph." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/569.

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Information extraction (IE) mainly focuses on three highly correlated subtasks, i.e., entity extraction, relation extraction and event extraction. Recently, there are studies using Abstract Meaning Representation (AMR) to utilize the intrinsic correlations among these three subtasks. AMR based models are capable of building the relationship of arguments. However, they are hard to deal with relations. In addition, the noises of AMR (i.e., tags unrelated to IE tasks, nodes with unconcerned conception, and edge types with complicated hierarchical structures) disturb the decoding processing of IE. As a result, the decoding processing limited by the AMR cannot be worked effectively. To overcome the shortages, we propose an Interactive Information Extraction (InterIE) model based on a novel Semantic Information Graph (SIG). SIG can guide our InterIE model to tackle the three subtasks jointly. Furthermore, the well-designed SIG without noise is capable of enriching entity and event trigger representation, and capturing the edge connection between the information types. Experimental results show that our InterIE achieves state-of-the-art performance on all IE subtasks on the benchmark dataset (i.e., ACE05-E+ and ACE05-E). More importantly, the proposed model is not sensitive to the decoding order, which goes beyond the limitations of AMR based methods.
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Milward, David, and James Thomas. "From information retrieval to information extraction." In the ACL-2000 workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/1117755.1117767.

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Wu, Fei, Raphael Hoffmann, and Daniel S. Weld. "Information extraction from Wikipedia." In the 14th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401978.

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Rau, Lisa F. "Information extraction and evaluation." In the 5th conference. Morristown, NJ, USA: Association for Computational Linguistics, 1993. http://dx.doi.org/10.3115/1072017.1072053.

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Huang, Jing, Geoffrey Zweig, and Mukund Padmanabhan. "Information extraction from voicemail." In the 39th Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1073012.1073051.

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Sundheim, Beth M. "Information extraction system evaluation." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1993. http://dx.doi.org/10.3115/1075671.1075782.

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Reports on the topic "Information Extraction"

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Etzioni, Oren. Open Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada538482.

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2

Cohen, Eric, and Evelyne Tzoukermann. Phrase-based Multimedia Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada456800.

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3

White, Michael, Tanya Korelsky, Claire Cardie, Vincent Ng, David Pierce, and Kiri Wagstaff. Multidocument Summarization via Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada457772.

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4

Onyshkevych, Boyan. Template Design for Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, July 1993. http://dx.doi.org/10.21236/ada635849.

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5

Srihari, Rohini, and Wei Li. Information Extraction Supported Question Answering. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada460042.

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6

Shinyama, Yusuke, and Satoshi Sekine. Paraphrase Acquisition for Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada460236.

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7

Irwin, N. H., S. M. DeLand, and S. V. Crowder. Extraction of information from unstructured text. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/148697.

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8

Nurre, Joseph H. Automate Information Extraction from Scan Data. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada362095.

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9

Principe, Jose C. Feature Extraction Using an Information Theoretic Framework. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada397483.

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10

Irwin, N. H. Domain-independent information extraction in unstructured text. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/378821.

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