More Details

Summary:

This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics. Both web search and big data analytics aim to fulfill peopleśℓℓ needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines.

Author/Creator:

Wachsmuth, Henning , author

Languages:

English

Language Notes:

Item content: English

Main Work:

Text analysis pipelines: towards ad-hoc large scale text mining [by Wachsmuth, H.]

Related Series:

Lecture notes in computer science; [volume] 9383

LNCS sublibrary > SL 1, Theoretical computer science and general issues

Subjects:

Data mining

Text processing (Computer science)

Computer science

Computers

Logic, Symbolic and mathematical

Database management

Information retrieval

Artificial intelligence

Physical Description:

1 online resource (xx, 302 pages) : illustrations.

Digital Characteristics:

text file

Call Numbers:

QA76.9.T48 W334 2015eb

ISBNs:

9783319257419 (electronic bk.)
3319257412 (electronic bk.)
9783319257402 (Print) [Invalid]
3319257404
9783319257402

Other Standard Numbers:

Digital Object Identifier: 10.1007/978-3-319-25741-9

OCLC Numbers:

933755611