INFORMATION EXTRACTION: A BRIEF INTRODUCTION in Visual Studio .NET

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INFORMATION EXTRACTION: A BRIEF INTRODUCTION
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Figure 3.1 HLT and the Semantic Web.
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faced, thus justifying the need for the so-called Ontology-Based IE approaches. Section 3.5 presents a number of these approaches, including some graphical user interfaces. Controlled Language IE (CLIE) is then presented as a high-precision alternative to information extraction from unrestricted, ambiguous text. The chapter concludes with a discussion and outlines future work.
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3.2. INFORMATION EXTRACTION: A BRIEF INTRODUCTION
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Information extraction (IE) is a technology based on analysing natural language in order to extract snippets of information. The process takes texts (and sometimes speech) as input and produces xed format, unambiguous data as output. This data may be used directly for display to users, or may be stored in a database or spreadsheet for later analysis, or may be used for indexing purposes in information retrieval (IR) applications such as internet search engines like Google. IE is quite different from IR:  an IR system nds relevant texts and presents them to the user;  an IE application analyses texts and present only the speci c information from them that the user is interested in. For example, a user of an IR system wanting information on trade group formations in agricultural commodities markets would enter a list of relevant words and receive in return a set of documents (e.g., newspaper articles) which contain likely matches. The user would then read the
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SEMANTIC ANNOTATION AND HUMAN LANGUAGE TECHNOLOGY
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documents and extract the requisite information themselves. They might then enter the information in a spreadsheet and produce a chart for a report or presentation. In contrast, an IE system would automatically populate the spreadsheet directly with the names of relevant companies and their groupings. There are advantages and disadvantages with IE in comparison to IR. IE systems are more dif cult and knowledge-intensive to build, and are to varying degrees tied to particular domains and scenarios. IE is more computationally intensive than IR. However, in applications where there are large text volumes IE is potentially much more ef cient than IR because of the possibility of dramatically reducing the amount of time people spend reading texts. Also, where results need to be presented in several languages, the xed-format, unambiguous nature of IE results makes this relatively straightforward in comparison with providing the full translation facilities needed for interpretation of multilingual texts found by IR. Useful overview sources for further details on IE include: Cowie and Lehnert (1996), Appelt (1999), Cunningham (2005), Gaizauskas and Wilks (1998) and Pazienza (2003).
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3.2.1. Five Types of IE
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IE is about nding ve different types of information in natural language text: 1. Entities: things in the text, for example people, places, organisations, amounts of money, dates, etc. 2. Mentions: all the places that particular entities are referred to in the text. 3. Descriptions of the entities present. 4. Relations between entities. 5. Events involving the entities. For example, consider the text:
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Ryanair announced yesterday that it will make Shannon its next European base, expanding its route network to 14 in an investment worth around s180m. The airline says it will deliver 1.3 million passengers in the rst year of the agreement, rising to two million by the fth year .
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To begin with, IE will discover that Shannon and Ryanair are entities (of types location and company, perhaps), then, via a process of reference resolution, will discover that it and its in the rst sentence refer to Ryanair (or are mentions of that company), and the airline and it in the second sentence also refer to Ryanair. Having discovered the mentions descriptive information can be extracted, for example that Shannon is a European base. Finally relations, for example that Shannon will be a base
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INFORMATION EXTRACTION: A BRIEF INTRODUCTION
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of Ryanair, and events, for example that Ryanair will invest s180 million in Shannon. These various types of IE provide progressively higher-level information about texts. They are described in more detail below; for a thorough discussion and examples see Cunningham (2005).
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