A Comparative Study of Information Extraction Strategies

Feldman, Ronen; Aumann, Yonatan; Finkelstein-Landau, Michal; Hurvitz, Eyal; Regev, Yizhar; Yaroshevich, Ariel

doi:10.1007/3-540-45715-1_36

Cited by 28 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Information extraction systems fall into two distinct categories; Rule-Based (hand-crafted) and Machine Learning systems (Feldman et al 2002). During the seven Machine Understanding Conferences (MUC), the involvement of rule-based information extraction systems has been influential.…”

Section: Information Extractionmentioning

confidence: 99%

“…Hand-crafted rules make use of domain knowledge and domain-independent linguistic syntax, in order to negotiate semantics and pragmatics in context and to extract information for a defined problem. It is reported that rule-based systems can achieve high levels of precision of between 80%-90% when identifying general purpose entities such as 'Person', 'Location', and 'Organisation' from financial news documents (Feldman et al 2002;Lin 1995).…”

Section: Rule-based Information Extraction Systemsmentioning

confidence: 99%

“…For this reason rule-based systems have been criticised by some as being costly and inflexible, having limited portability and adaptability to new IE scenarios (Feldman et al 2002). However, developers of rule-based systems claim that, depending on the IE task, the linguistic complexity can be bypassed and a small number of rules can be used to extract large sets of variant information (Hobbs et al 1993).…”

Section: Rule-based Information Extraction Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Archaeological Discriminant Applications of the Lubischew Test

Guerrero¹,

Serrano²,

Jiménez‐Arenas³

2015

Mathematics and Archaeology

View full text Add to dashboard Cite

IntroductionArchaeologists generate large quantities of text, ranging from unpublished technical fieldwork reports (the 'grey literature') to synthetic journal articles. However, the indexing and analysis of these documents can be time consuming and lacks consistency when done by hand. It is also rarely integrated with the wider archaeological information domain, with bibliographic searches having to be undertaken independently of database queries, for example. Text mining offers a means of extracting information from large volumes of text, providing researchers with an easy way of locating relevant texts and also of identifying patterns in the literature. In recent years techniques of Natural Language Processing (NLP) and its subfield, Information Extraction (IE), have been adopted to allow researchers to find, compare and analyse relevant documents, and to link them to other types of data. This chapter introduces the underpinning mathematics and provides a short presentation of the algorithms and distance measures used, from the point of view of artificial intelligence and computational logic. It describes the different NLP schools of thought and compares the pros and cons of rule-based vs machine learning approaches to information extraction. The role of ontologies and named entity recognition will be discussed and the chapter demonstrates how IE can provide the basis for semantic annotation and how it contributes to the construction of a semantic web for archaeology. The authors have worked on a number of projects that have employed techniques from NLP and IE in Archaeology, including Archaeotools, STAR and STELLAR. The chapter describes the archaeological user needs requirement, drawing examples from several countries, and the authors present examples drawn from their own projects, and previous work by others, of how NLP and IE can contribute to addressing this need. The problems and challenges of employing text mining in the archaeological domain are discussed, as well as the potential benefits.

show abstract

Section: Information Extractionmentioning

confidence: 99%

Section: Rule-based Information Extraction Systemsmentioning

confidence: 99%

Section: Rule-based Information Extraction Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Archaeological Discriminant Applications of the Lubischew Test

Guerrero¹,

Serrano²,

Jiménez‐Arenas³

2015

Mathematics and Archaeology

View full text Add to dashboard Cite

show abstract

“…The problem of identifying, extracting and storing information from unstructured documents is widely recognized as a main issue in the field of information and knowledge management and has been extensively studied in the literature (see, for instance, [13,16,33,31,17]). Most existing approaches use regular expressions as a convenient mean for extracting information from text automatically.…”

Section: Motivationmentioning

confidence: 99%

On the complexity of regular-grammars with integer attributes

Manna

Scarcello

Leone

2011

Journal of Computer and System Sciences

View full text Add to dashboard Cite

Regular grammars with attributes overcome some limitations of classical regular grammars, sensibly enhancing their expressiveness. However, the addition of attributes increases the complexity of this formalism leading to intractability in the general case. In this paper, we consider regular grammars with attributes ranging over integers, providing an in-depth complexity analysis. We identify relevant fragments of tractable attribute grammars, where complexity and expressiveness are well balanced. In particular, we study the complexity of the classical problem of deciding whether a string belongs to the language generated by any attribute grammar from a given class C (call it parse [C] ). We consider deterministic and ambiguous regular grammars, attributes specified by arithmetic expressions over {| |, +, −, ÷, %, * }, and a possible restriction on the attributes composition (that we call strict composition). Deterministic regular grammars with attributes computed by arithmetic expressions over {| |, +, −, ÷, %} are P-complete. If the way to compose expressions is strict, they can be parsed in L, and they remain tractable even if multiplication is allowed. Problem parse [C] becomes NP-complete for general regular grammars over {| |, +, −, ÷, %, * } with strict composition and for grammars over {| |, +, −, ÷, %} with unrestricted attribute composition. Finally, we show that even in the most general case the problem is in polynomial space.

show abstract

“…Existing research on Web data extraction has produced a number of techniques ( [1][2][3][4][5][6][7][8][9][10]). The current dominate technique is wrapper induction based on inductive machine learning.…”

Section: Fig 1 An Example Product Description Pagementioning

confidence: 99%