Adaptive information extraction

Abstract

The growing availability of on-line textual sources and the potential number of applications of knowledge acquisition from textual data has lead to an increase in Information Extraction (IE) research. Some examples of these applications are the generation of data bases from documents, as well as the acquisition of knowledge useful for emerging technologies like question answering, information integration, and others related to text mining. However, one of the main drawbacks of the application of IE refers to its intrinsic domain dependence. For the sake of reducing the high cost of manually adapting IE applications to new domains, experiments with different Machine Learning (ML) techniques have been carried out by the research community. This survey describes and compares the main approaches to IE and the different ML techniques used to achieve Adaptive IE technology.

Automatic Extraction of Attributes and Entities for Product Differentiation

“…Extracting information from unstructured text that is natural language documents have been proposed [2,3]. A template independent wrapper has been developed which exercises labeled news pages from a single site [4].…”

Hidden Markov Models and Text Classifiers for Information Extraction on Semi-Structured Texts

“…Information Extraction (IE) is concerned with extracting relevant data from a collection of documents [27]. An IE system identifies document fragments that correctly fill in slots in a given output form.…”

“…When querying Web search engines (e.g., Google), for instance, the user has to browse the retrieved (relevant or not) documents one by one, looking for the desired data. In this context, Information Extraction (IE) systems arise as a means to facilitate the information access, by extracting from the documents only the parts that correctly fill in a set of pre-defined output slots (data fields) [27]. The extracted data can be directly presented to the user or can be stored in appropriate databases.…”

Automatic Information Extraction in Semi-structured Official Journals

“…A great amount of valuable information is stored in digital repositories of textual documents [1]. A significant part of the information comprised in these repositories is only legible by humans, being hardly manipulated by computer machines.…”

“…Documents which present the above-cited characteristics are called semi-structured texts [1]. In order to manipulate such documents, an automatic system called Information Extraction (IE) system may be very suitable.…”