Information Extraction: Fundamentals and Applications

By Fouad Sabry

What Is Information Extraction

The process of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources is referred to as information extraction (IE). This activity, in the vast majority of instances, refers to the processing of documents written in human languages by utilizing natural language processing (NLP). The process of extracting information can be seen in recent activity in multimedia document processing such as automatic annotation and content extraction out of photos, audio, and video documents.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Information extraction

Chapter 2: Natural language processing

Chapter 3: Text mining

Chapter 4: Named-entity recognition

Chapter 5: Unstructured data

Chapter 6: Relationship extraction

Chapter 7: Data extraction

Chapter 8: Knowledge extraction

Chapter 9: Entity linking

Chapter 10: Outline of natural language processing

(II) Answering the public top questions about information extraction.

(III) Real world examples for the usage of information extraction in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of information extraction' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of information extraction.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 5, 2023

Book preview

Chapter 1: Information extraction

When dealing with machine-readable papers and other electronically represented sources, information extraction (IE) is the process of mechanically extracting structured information. This often involves some kind of natural language processing applied to materials written in the English language (NLP). Information extraction may be considered as a recent trend in multimedia document processing, including automated annotation and content extraction from photos, audio, and video.

Current methods to IE (as of 2010) concentrate on limited domains due to the complexity of the issue. Take, for instance, the extraction of formal relations such as those denoting business mergers from newswire stories:

{\displaystyle \mathrm {MergerBetween} (company_{1},company_{2},date)}

, from a paragraph of web news like:

Foo Inc., located in New York, yesterday announced that it has acquired Bar Corp.

One of IE's main aims is to make it possible to do computations on data that was previously in an unstructured form. One aim is to enable machine reasoning about the incoming data's logical structure. Data from a certain domain that is well-defined semantically and can be evaluated in terms of category and context is considered structured data.

Extracting useful data from large amounts of text is just one piece of the jigsaw that is robotic text management, that which is transmitted, archiving and presenting.

Information retrieval (IR) is a field that has created automated techniques for, flavoring often associated with statistics, indexing and organizing massive document repositories.

Natural language processing (NLP) is a complementary method that has achieved remarkable progress in modeling human language processing despite the enormous scale of the undertaking.

Both in terms of challenge and importance, In between IR and NLP, IE handles jobs in between.

To what extent the inputs, In order for IE to function, a collection of documents with consistent formatting must already exist, i.e.

provides information that is comparable to that found in other sources but with some key differences.

An example, Take, for example, a collection of news items on terrorism in Latin America, all of which are predicated on the assumption that one or more actual acts of terrorism occurred.

We also provide a template for use with any IE endeavor, to save the data from a single document in a case frame or frames.

In the context of terrorism, Those responsible might be written into a form with blanks for other details, victim, and instrument of the crime of terrorism, and the actual day when it occurred.

An IE system for this problem is required to understand an attack article only enough to find data corresponding to the slots in this template.

In the late 1970s, when natural language processing was just getting started, researchers began focusing on the problem of information extraction:

Navy Messages MUC-1 (1987) and MUC-3 (1989).

Terrorism in Latin American countries: MUC-3 (1991) and MUC-4 (1992).

Microelectronics and Joint Ventures, MUC-5 (1993).

Management Change News Articles, MUC-6 (1995).

Reports on the MUC-7 Satellite Launch from 1998.

The U.S. Defense Advanced Research Projects Agency (DARPA) provided significant funding because it wanted to eliminate the need for human analysts to undertake routine jobs like reading the newspaper for terrorist linkages.

The increasing availability of data in an unstructured format is directly related to the current importance of IE. The current Internet is what its creator, Tim Berners-Lee, calls the network of documents.

Creating a structured perspective of the information inherent in free text necessitates applying information extraction to the text, which is tied to the issue of text simplification. The idea is to make the text more machine-readable so that the phrases can be processed more efficiently. Common IE Duties and Responsibilities:

Template filling is the process of extracting a predetermined set of data from a document, such as the names of the terrorists, the names of the victims, the date and time of the attack, etc.

For each input document, extract one or more event templates. A newspaper story, for instance, may detail many separate terrorist incidents.

To populate a knowledge base with information from a specified collection of documents. Databases often consist of triangular sets, where each set consists of three parts (entity, relation, entity 2). (Barack Obama, Spouse, Michelle Obama)

Named-entity recognition uses prior domain knowledge or information taken from previous sentences to identify certain sorts of words, such as proper names, places, times, and numbers. The recognition process often include labeling the extracted object with a specific name. Entity detection is a simpler problem that seeks to find instances of entities without prior knowledge of them. When processing the sentence M. Smith enjoys fishing, named entity detection would mean recognizing that M. Smith refers to a person without also recognizing that a particular M. Smith is (or could be) the subject of the sentence.

Resolution of coreference entails finding synonymy and antonymy in text. This is often limited to locating associations between named things that have already been retrieved in IE tasks. International Business Machines and IBM, for instance, are both shorthand references to the same physical organization. By combining the two phrases Fishing is a hobby for M. Smith. It would be helpful to recognize that when someone says but he doesn't enjoy riding, he is referring to the person M. Smith, who has already been identified..

Extraction of relationships between entities: finding links between things like:

A HUMAN BEING is employed by a BUSINESS. (Taken from the phrase Bill is employed at IBM.)

LOCATION-based INDIVIDUAL (extracted from the sentence Bill is in France.)

The term semi-structured information extraction (IE) may be used to describe any method of retrieval that attempts to reconstruct a publication's original information structure:

Finding and removing tables from documents is called table extraction..

Information extraction from tables: systematically removing data from tables. Interpreting the cells, rows, columns, connecting information within the table, and understanding the information displayed in the table are all additional steps that make table information extraction a more complicated operation than simple table extraction alone.

Extracting comments from articles' body text to re-establish authorship attributions is known as comment extraction.

Syntax and semantics of language

Extraction of Terminology: Identifying Useful Words in a Collection

Audio extraction

An example of music extraction using a template would be the retrieval of a time index for each occurrence of a percussive sound in order to reflect the song's main rhythmic component.

Keep in mind that this is by no means an all-inclusive list, that the precise definition of IE activities is not universally agreed upon, and that many methods combine many IE sub-tasks in order to accomplish a larger purpose. Common methods utilized in IE include machine learning, statistical analysis, and NLP.

Information extraction (IE) from multimedia documents can now be stated in a high-level framework, similar to what is done with text, making this an exciting area of study. As a result, it's inevitable that data taken from many documents and sources will eventually be combined.

The MUC conventions have centered on IE. However, the rapid growth of the Web has increased the need of creating IE systems to assist users in making sense of the vast quantities of information accessible online. Systems that conduct IE from online text need to be low-priced, flexible-in-design, and simple-to-adapt to new domains. Those requirements can't be fulfilled by MUC systems. Furthermore, the HTML/XML tags and layout conventions included in online texts are not used by linguistic analysis done for unstructured text. As a consequence, less linguistically demanding methods of extracting a page's content have been created for IE on the Web utilizing wrappers. Wrapper development is a complex and time-consuming process that traditionally has been done by hand. Inducing such rules automatically has been attempted using supervised and unsupervised machine learning methods.

Wrappers manage collections of web pages that have a lot of structure, such product catalogs