GRISHMAN, R., ed. Information Extraction: Techniques and Challenges - Information Extraction - a Multidisciplinary Approach to an Emerging Information Technology. Lecture Notes in Artificial Intelligence, ed. M.T. PAZIENZA. 1997, Springer-Verlag: Berlin, Heldelberg. 10-27.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....can be harnessed to indentify logical representations of news from news reports in the form of free text. Information extraction is based on natural language processing, and by focussing on restricted domains such as on news reports from online newsfeeds, it can give impressive performance [8, 14, 2]. However, handling textual news reports is only one application of ramification analysis. Potentially, the approach could be used by intelligent systems such as robotic systems, surveillance systems, and monitoring systems, where the news is obtained from various kinds of sensors such as ....

....An instantiated Template for Summary company target Marsem Pharmaceuticals Inc. company predator Schein Pharmaceuticals Inc. type of takeover Friendly value 240 million dollars Table 5: An instantiated Template for Takeover highlighting pertinent points, and removing superfluous information [8, 14, 2]. In this section, we consider integrating ramification analysis with information extraction, by feeding extracted information directly into a ramification analysis knowledgebase. As an example of an information extraction system, we consider LOLITA (Large scale Objectbased Linguistic Interactor ....

R Grishman. Information extraction techniques and challenges. In M Pazienza, editor, Information extraction. Springer, 1997.

....our approach in translation to logical form. Section 5 describes in details the templates structure. Section 6 explains the algorithm for filling templates with information from the text. Sec tion 7 contains the conclusion. 2 Information extraction IE can be divided into the following subtasks [6]: Lexical Analysis, which turns a text into a sequence of sentences, each of them is a sequence of lexical items (tokens) Usually sentences are not marked, so special techniques are required to recognise sentence boundaries. Each token is looked up in the dictionary to determine its possible ....

....The arguments to be extracted often correspond to noun phrases in the text, and relationships to grammatical functional relations. Note that for IE we are only interested in the grammatical relations relevant to the template; correctly determining the other relations may be a waste of time [6]. Template (Scenario) Pattern Matching, which maps the syntactic structures to semantic structures related to the templates to be filled in. This stage extracts the events or relationships relevant to the scenario. The max score result reported for this task is f measure 57 . One of the most ....

Grishman, Ralph (1997), "Information Extraction: Techniques and Challenges", International Summer School, SCIE-97

.... the task of extracting instances of a prede ned class of events (e.g. management succession events) from natural language texts, building a structured and unambiguous representation of the entities participating in these events (e.g. people, positions, companies) and the relations between them [2]. Information Extraction and its methods of evaluation have to a great extent been de ned by the Message Understanding Conferences (MUCs) 3, 4, 5, 6, 7] While Information Retrieval (i.e. document retrieval) systems aim at returning a ranked list of documents as an answer to any arbitrary ....

Ralph Grishman. Information Extraction: Techniques and challenges. In Maria Teresa Pazienza, editor, Information Extraction - A Multidisciplinary Approach to an Emerging Information Technology, pages 1027. Springer, 1997.

....sources. This is usually much more difficult than schema matching or wrapper induction, but is of great relevance, as many information is stored as natural text only. Information extraction has a long history in Artificial Intelligence and Natural Language Processing. For a brief overview refer to [2]. 2.1.2. Dealing with massive amounts of data After several data sources are connected, there is still the problem of processing the underlying data. The methods needed to process huge amounts of data often differ completely from methods to handle smaller amounts of data as they have to be much ....

Grishman, R. : Information Extraction: Techniques and Challenges, SCIE-97.

....precision of 55.4 and a recall of 50.0 . This performance compares favorably with the performance of DARPA sponsored Message Understanding Conferences (MUC) systems, 8 where good performance in simpler event extraction domains translated to precision and recall measurements between 50 and 70 [ 14]. We present two examples of false positives that underscore the difficulty in automatic extraction of acquisition events that result in changes in equity holdings. 9 In many cases, the language in 10 K documents is so ambiguous that even humans are confused. The first example comes from a ....

Grishman, R. 1997. Information Extraction: Techniques and Challenges. Information Extraction (International Summer School SCIE-97), M.T. Pazienza, ed. New York: Springer-Verlag.

....Wide Web is such a collection. In Formal Concept Analysis the term property has a special meaning similar to attribute. In this paper property is only be used with the meaning of real estate property, e.g. a house or apartment. 1. 1 Definitions Information Extraction The objective of IE [13] is to locate and identify specific information from a natural language document. The key element of IE systems is the set of extraction rules, or extraction patterns, that identify the target information according to a scenario. Once an extraction pattern is identified, the IE system reduces ....

....approach when extracting the rules, such as CRYSTAL [21] or a supervised algorithm along with a top bottom approach, such as WHISK [22] and SRV [11] Interestingly, with respect to this paper, WHISK used real estate classified ads as its document collection. Finally, another system named PROTEUS [13] used dictionaries along with a set of regular expressions to mine documents in a top bottom approach. Simultaneous with these developments, the wrapper generation communities also developed some IE systems using machine learning algorithms to generate extraction patterns for online information ....

Grishman R., Information Extraction: Techniques and Challenges, New York University, 18 pp., 1997.

....Information extraction systems produce a structured representation of the information that is buried in unstructured text documents. Improving the efficiency of information extraction systems over large text databases is the focus of this paper. In general, state of the art extraction systems [16] apply many rules over each available text segment to determine whether the segment can be used to fill a value of an attribute in a tuple. Therefore, processing each document is relatively expensive, and typically involves several steps such as named entity tagging (e.g. identifying person names ....

....in location l. Figure 1 shows the basic stages in the extraction of a tuple from a document fragment. We omit the more sophisticated post processing and analysis performed by many state of the art information extraction systems, as this is beyond the scope of this discussion. Refer to [16] for an in depth discussion. QXtract stands for Querying for eXtraction. As one of the first stages of extraction, the input documents are typically passed through a named entity tagger, which is able to recognize entities (e.g. organizations, locations, and persons) Named entity tagging is ....

R. Grishman. Information extraction: Techniques and challenges. In Information Extraction (International Summer School SCIE-97). Springer-Verlag, 1997.

....rules corresponds to the named entities (person, organisation, location names) and fixed data structures (date, time and monetary expressions) traditionally identified by any NE recognition system, which are largely domain independent. Other rule based NE recognition systems such as Proteus [12] and FASTUS [3] do not seem to have this flexibility of design, and therefore are much harder to adapt to new domains and applications. Current performance of the NE recognition system is around 90 95 Precision and Recall, which is similar to other current systems. In the following section we ....

R. Grishman. Information Extraction: Techniques and Challenges. In Information Extraction: a Multidisciplinary Approach to an Emerging Information Technology, Springer 1997.

....time is usually spent on further reading and analyzing of the texts in order to extract the facts of interest. Information Extraction (IE) is the process of analyzing unstructured texts and extracting the information relevant to some problem into a structured representation, or as described in [Grishman97] the process of selective information structuring. The information relevant to the problem is usually divided into: entities persons, organizations, locations, etc. that are located in the text attributes that are related to the entities (e.g. the title of the person or the type of the ....

Ralph Grishman: "Information Extraction.' Techniques and Challenges". Springer-Verlag, Lecture Notes in Artificial Intelligence, Rome, 1997.

....on demand. It is envisaged that the server will be improved to include the ability to query the audio database by topic, speaker, entity and so on. These functionalities will build upon existing tools such as those for automatic speech recognition, topic detection [14] and information extraction [6]. The underlying network structure assumed by the Magic Lounge Audio server and its corresponding audio tool is MBone. The basic client side tools are adapted from the existing MBone audio tools which are based on IETF (Internet Engineering Task Force) standards [7] and allow both point to point ....

R. Grishman, "Information Extraction: Techniques and Challenges," Lecture Notes in Computer Science, Vol. 1299, 1997.

....from the documents. We believe that the most characteristic factors, which describe a document, are the terms and events mentioned within the document. It is very important to extract the right features in order to have accurate and useful results. Information Extraction (IE) is the technology [6], which involves in this preprocessing step. The resulting document representations are used as input to a clustering algorithm. We have developed a clustering algorithm appropriate for categorical data. Using this algorithm we are in a position to discover structure within the document ....

Grishman R. (1997), "Information Extraction: Techniques and Challenges", International Summer School, SCIE-97

....2. word spotting in each segment (subsection 3.1) 3. contextual tagging using the relative position of the units with relation to already tagged segments (subsection 3. 2) Notice that the design methodology used for our system is dioeerent from typical Information Extraction approaches [9] in the sense that, instead of trying to nd some specic information in a whole document, it rather tries to identify the nature of the information expressed by each single piece of the text. In addition, the general strategy used by traditional systems [1, 8] consists in searching 1 for example, ....

R. Grishman. Information extraction: Techniques and challenges. In ed. M. T. Pazienza, editor, International Summer School SCIE-97, Springer-Verlag, July 1997.

....filling are explicitly represented, while other relations are underspecified or even left implicit. In other words, a SIEA is the minimal approximation of a complete parse tree providing all the relations useful for i.e. Hence, differently from many current systems that perform just partial parsing [10, 8], the parser performs a kind of full text parsing [2] Since parsing is deterministic, just one structure is produced for [Tsent ] dep , Tsent ] feat and [Tsent ] lf . ffl Inference: derivation of additional information in [T ] lf not explicitly mentioned in the text, that can be derived by ....

Ralph Grishman, `Information extraction: Techniques and challenges', in Information Extraction: a multidisciplinary approachto an emerging technology, ed., M. T. Pazienza, Springer Verlag, (1997).

....names in text and their classification as different types of named entity, e.g. persons, organizations, locations. NER is evaluated as a separate task at the international evaluation conferences for IE (Message Understanding Conferences MUC [4] NER is not only an important subtask in IE [8] but also in lexical acquisition for the development of robust natural language processing systems [5] Moreover NER may prove fruitful for tasks such as indexing of documents and maintenance of data bases containing information for the identified named entities. The lexical resources that are ....

Grishman, R. Information Extraction: Techniques and Challenges (1997) in Pazienza M-T. ed. Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, SCIE-97, Frascatti, Italy, July 1997, pp. 1026.

....states, actions, and attributes, that could be conveyed by the items of structured text. Much material is potentially available as structured text. This includes items of text structured using XML tags, and the output from information extraction systems given in templates (see for example [CL96, Gri97, ARP98] The notion of structured text also overlaps with semi structured data (for reviews see [Abi97, Bun97] Whilst structured text is useful as a resource, there is a need to develop techniques to handle, analyse, and reason with it. Here we focus on inconsistencies that can arise between ....

R Grishman. Information extraction techniques and challenges. In M Pazienza, editor, Information Extraction. Springer, 1997.

....detecting texts relevant for a single class (e.g. management succession) with results ranging between 80 95 for both precision and recall. But IE cannot be performed on a large number of classes: it is an expensive technology as it requires a large amount of time of linguistically aware personnel [Grishman, 1997]. Attempts at separating linguistic knowledge (e.g. syntactic knowledge) from domain dependent knowledge (e.g. the domain patterns) Grishman, 1997; Hobbs et al. FACILE: Classifying Texts Integrating Pattern Matching and Information Extraction 1 Fabio Ciravegna Alberto Lavelli Nadia Mana ....

.... be performed on a large number of classes: it is an expensive technology as it requires a large amount of time of linguistically aware personnel [Grishman, 1997] Attempts at separating linguistic knowledge (e.g. syntactic knowledge) from domain dependent knowledge (e.g. the domain patterns) [Grishman, 1997; Hobbs et al. FACILE: Classifying Texts Integrating Pattern Matching and Information Extraction 1 Fabio Ciravegna Alberto Lavelli Nadia Mana ITC irst Loc. Pant di Povo 38050 Trento Italy Johannes Matiasek FAI Schottengasse 3 1010 Vienna Austria Luca Gilardoni Silvia Mazza Massimo ....

R. Grishman. Information Extraction: Techniques and Challenges. In: M. T. Pazienza (ed.). Information Extraction: a multidisciplinary approach to an emerging technology. Springer-Verlag, 1997.

....reasonably well even if certain instances of a tuple are missed, as long as the system captures one such instance. This approach is in contrast with the goals of traditional information extraction research, where a system attempts to extract as much information as possible from each document [Gri97] DIPRE, on the other hand, attempts to build the most comprehensive table from all of the documents in the collection. In [AG00] we built on this approach and introduced Snowball. We developed a method for defining and representing extraction patterns that is at the same time flexible, so that ....

....DIPRE method and our Snowball system both address issues that have long been the subject of information extraction research. However, DIPRE and Snowball do not attempt to extract all the relevant information from each document, which has been the goal of traditional information extraction systems [Gri97, FSM 95] One of the major challenges in information extraction is the necessary amount of manual tagging involved in training the system for each new task. Ril96] generates extraction patterns automatically by using a training corpus of documents that were manually marked as either relevant ....

Ralph Grishman. Information extraction: Techniques and challenges. In Information Extraction (International Summer School SCIE-97). Springer-Verlag, 1997.

....their filler subparts (such as named entities or NEs) the rules for filling them, and associated knowledge structures, as rapidly as possible for new domains and genres. IE as a modern language processing technology was developed largely in the US, but with strong development centres elsewhere [7, 8, 9, 10]. Over 25 systems, world wide, have participated in the recent DARPA sponsored MUC and TIPSTER IE competitions, most of which have a generic structure [9] Previously unreliable tasks of identifying template fillers such as names, dates, organizations, countries, and currencies automatically ....

R. Grishman, Information extraction: Techniques and challenges. In M-T. Pazienza, (ed.), Proceedings of the Summer School on Information Extraction (SCIE-97), LNCS/LNAI. Springer-Verlag, 1997.

....apply to text corpora with the aid of extraction rules that seek those fillers in the corpus, given a set of syntactic, semantic and pragmatic constraints. IE as a modern language processing technology was developed largely in the US. but with strong development centres elsewhere [18] 19] [30], 34] 27] Over 25 systems world wide, have participated in the recent MUC competitions, most of which have a generic structure [34] and previously unreliable tasks of identifying, names, dates, organizations, countries, and currencies automatically often referred to as TE, or Template ....

R. Grishman. Information extraction: Techniques and challenges. In MT. Pazienza, editor, Proceedings of the Summer School on Information Extraction (SCIE-97), LNCS/LNAI. Springer-Verlag, 1997.

....that could be conveyed by the items of structured text. Much material is potentially available as structured text. This includes items of text structured using XML tags (see for example [GQ99, Pfa99] the output from information extraction systems given in templates (see for example [CL96, Gri97, ARP98] and databases used by some online news agencies where journalists file reports as structured text and these entries are used by editors to generate free text news reports in different languages. The notion of structured text also overlaps with semi structured data (for reviews see ....

R Grishman. Information extraction techniques and challenges. In M Pazienza, editor, Information Extraction. Springer, 1997.

....en extraction d information a joui d une grande popularite. Aux Etats Unis, l armee s est particulierement interessee a l extraction d information (d ou l organisation des Message Understanding Conferences) et de nombreux projets de recherche ont vu le jour, par exemple [Hobbs et al. 1996, Grishman, 1997] De son cote, l Europe s est aussi interessee au domaine et de nombreux systemes ont ete developpes pour repondre aux besoins de l industrie [Gaizauskas et Humphreys, 1997, Ecran, 1998] En ce qui concerne l extraction multilingue, un certain nombre de travaux ont ete e#ectues ; cependant, il ....

Grishman, R. (1997). Information Extraction : Techniques and Challenges. Dans

....reasonably well even if certain instances of a tuple are missed, as long as the system captures one such instance. This approach is in contrast with the goals of traditional information extraction research, where a system attempts to extract as much information as possible from each document [13]. DIPRE, on the other hand, attempts to build the most comprehensive table from all of the documents in the collection.In [1] we built on this approach and introduced Snowball. We developed a method for defining and representing extraction patterns that is at the same time flexible, so that we ....

....DIPRE method and our Snowball system both address issues that have long been the subject of information extraction research. However, DIPRE and Snowball do not attempt to extract all the relevant information from each document, which has been the goal of traditional information extraction systems [13, 10]. One of the major challenges in information extraction is the necessary amount of manual tagging involved in training the system for each new task. 14] generates extraction patterns automatically by using a training corpus of documents that were manually marked as either relevant or irrelevant ....

R. Grishman. Information extraction: Techniques and challenges. In Information Extraction (International Summer School SCIE-97). SpringerVerlag, 1997.

....in this paper both address issues that have long been the subject of information extraction research. Our task, though, is different in that we do not attempt to extract all the relevant information from each document, which has been the goal of traditional information extraction systems [10]. One of the major challenges in information extraction is the necessary amount of manual labor involved in training the system for each new task. This challenge has been addressed in different ways. One approach is to build a powerful and intuitive graphical user interface for training the ....

Ralph Grishman. Information extraction: Techniques and challenges. In Information Extraction (International Summer School SCIE-97). Springer-Verlag, 1997.

....users who are domain experts to tailor the IE system to new extraction tasks, without the assistance of computational linguists. 6 Acknowledgments This paper is based on a paper previously published in International Workshop on Lexically Driven Information Extraction by Roman Yangarber and Ralph Grishman(Roman and Grishman 97) The Japanese Information Extraction system and the Japanese Proteus Extraction Tool is developed based on English systems. The English Proteus Information Extraction system was developed by Professor Ralph Grishman, and The English Proteus Extraction Tool was developed by ....

....Driven Information Extraction by Roman Yangarber and Ralph Grishman(Roman and Grishman 97) The Japanese Information Extraction system and the Japanese Proteus Extraction Tool is developed based on English systems. The English Proteus Information Extraction system was developed by Professor Ralph Grishman, and The English Proteus Extraction Tool was developed by Roman Yangarber. Also they helped us to develop the Japanese systems. We would greatly acknowledge them. This work is supported by Fujitsu Laboratory Ltd. ....

Ralph Grishman. Information extraction: Techniques and challenges. In Maria Teresa Pazienza, editor, Information Extraction. Springer-Verlag, Lecture Notes in Artificial Intelligence, Rome, 1997.

....to create general patterns, appropriate for text analysis. The present system operates on two tiers: ffl Proteus core extraction engine, an enhanced version of the one employed at MUC 6, 3] ffl PET GUI front end, through which the user interacts with Proteus, as described recently in [5, 6]) It is our hope that the example based approach will facilitate the customization of IE engines; we are particularly interested, as are other sites) in providing the non technical user such as a domain analyst, unfamiliar with system internals, with the capability to perform IE ....

....these include the general COMLEX syntactic dictionary, and domain specific lists of words and names. As the result, each token receives a reading, or a list of alternative readings, in case the token is syntactically ambiguous. A reading consists of For a detailed description of the system, see [3, 5] Figure 1: IE system architecture a list of features and their values (e.g. syntactic category = Noun ) LexAn optionally invokes a statistical part of speech tagger, which eliminates unlikely readings for each token. The next three phases operate by deterministic, bottom up, partial parsing, ....

Ralph Grishman. Information extraction: Techniques and challenges. In Maria Teresa Pazienza, editor, Information Extraction. Springer-Verlag, Lecture Notes in Artificial Intelligence, Rome, 1997.

....the knowledge bases. We are focusing on automatic techniques for creating the knowledge bases, without manually constructed training corpora. Phase I is an existing, self contained lower level component, implemented by Prof. Ralph Grishman at NYU for participation in MUC competitions, details in [19, 17]. Phases II and III provide higher level functionality to the overall IE 17 system, and form the substance of this thesis. We developed Phase II to address the problem of customization. Our experience with the interactive tools exposed the need for automatic corpus analysis, to provide a ....

....to narrow down the search space This is the question this research aims to answer. 3.1 Proteus: the Core IE Engine The system consists of a cascade of modules with their attendant knowledge bases. Each module transforms the input text document. For a detailed discussion of the system, see [17, 19]. There are four customizable knowledge bases in Proteus: Lexicon: contains scenario specific terms . Concept base: groups terms in a hierarchy of classes . Predicate base: describes the logical structure of events to be extracted . Pattern base: contains RE patterns that fire on events ....

[Article contains additional citation context not shown here]

Ralph Grishman. Information extraction: Techniques and challenges. In Maria Teresa Pazienza, editor, Information Extraction. Springer-Verlag, Lecture Notes in Artificial Intelligence, Rome, 1997.

....in situ, or as templates when more complex logical structures are extracted from the text and output separately, according to a predetermined format. 3 Architecture of the Proteus IE System Figure 2 shows the overall structure of the Proteus IE system. For a detailed discussion of the system, see [6, 7]. The system consists of a cascade of modules with their attendant knowledge bases, each of the modules applied to and transforming the input text document. The first module, lexical analysis is responsible for breaking up the document into sentences, and the sentences into tokens. This module ....

Ralph Grishman. Information extraction: Techniques and challenges. In Maria Teresa Pazienza, editor, Information Extraction. Springer-Verlag, Lecture Notes in Artificial Intelligence, Rome, 1997.

No context found.

GRISHMAN, R., ed. Information Extraction: Techniques and Challenges - Information Extraction - a Multidisciplinary Approach to an Emerging Information Technology. Lecture Notes in Artificial Intelligence, ed. M.T. PAZIENZA. 1997, Springer-Verlag: Berlin, Heldelberg. 10-27.

No context found.

Ralph Grishman. Information extraction: Techniques and challenges. In M. T. Pazienza, editor, Proceedings of the Information Extraction International Summer School

No context found.

Grishman, R.: Information extraction: Techniques and challenges. In Pazienza, M.T., ed.: Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology. LNAI 1299. Springer-Verlag, Heidelberg (1997) 10--27

No context found.

R. Grishman. Information Extraction: Techniques and Challenges. In M. T. Pazienza, Editor, Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, Springer, Berlin, 1997.

No context found.

R. Grishman. Information Extraction: Techniques and Challenges. In M. T. Pazienza, Editor, Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, Springer, Berlin, 1997.

No context found.

R. Grishman, "Information Extraction: Techniques and Challenges," in Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, J. G. Carbonell, Ed. Frascati, Italy: Springer, 1997, pp. 10-26.

No context found.

Ralph Grishman. Information extraction: Techniques and challenges. In SCIE, pages 10--27, 1997.

No context found.

R. Grishman. Information extraction: techniques and challenges. In M.T. Pazienza, editor, Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, International summer school, SCIE-97, volume 1299 of Lecture notes in computer science, pages 10--27. Springer-Verlag, Frascati, Italy, 1997. ISBN 354063438X

No context found.

Grishman, R. Information extraction: Techniques and challenges. M.T. Pazienza, Ed. Information Extraction. Springer-Verlag, New York, NY, 1997.

No context found.

R. Grishman. Information extraction: Techniques and challenges. In Information Extraction (International Summer School SCIE-97). Springer-Verlag, 1997.

No context found.

Ralph Grishman. Information extraction: Techniques and challenges. In SCIE, pages 10--27, 1997.

No context found.

R Grishman. Information extraction techniques and challenges. In M Pazienza, editor, Information Extraction. Springer, 1997.

No context found.

R. Grishman. Information extraction techniques and challenges. In: Information extraction, M. Pazienza (ed.), Springer, 1997.

No context found.

R Grishman. Information extraction techniques and challenges. In M Pazienza, editor, Information extraction. Springer, 1997.

No context found.

Grishman , R.1997. Information extraction: Techniques and challenges. In M.T. Pazienza, ed. Information extraction: a multidisciplinary approach to an emerging technology. Springer-Verlag.

No context found.

R Grishman. Information extraction techniques and challenges. In M Pazienza, editor, Information Extraction. Springer, 1997.

No context found.

Ralph Grishman. 1997. Information extraction: Techniques and challenges. In M. T. Pazienza, editor, Information Extraction: a multidisciplinary approach to an emerging technology.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC