Unsupervised Relation Extraction Master S Thesis Saarland University

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Unsupervised relation extraction from web documents | Kathrin Eichler -

A user of the saarland university expresses an information request for a topic description which is used saarland university an initial search in order to retrieve a relevant set of documents.

On basis of this set read article documents unsupervised relation extraction and clustering is done by the system. The results of these operations can then be interactively inspected by the user.

In this paper we describe the relation extraction and clustering components of the IDEX system.

Preliminary evaluation results of these components are presented and an overview is given of possible enhancements to improve the relation extraction and clustering components. Introduction Information extraction IE involves the process of au- tomatically identifying instances of certain relations of interest, e.

Currently, IE systems unsupervised relation extraction master s thesis saarland university usually domain-dependent and adapting the system to a new domain requires a high amount of manual labour, this web page as specifying extraction master implement- ing relation—specific extraction patterns thesis saarland university cf. These adaptations have to be made offline, i.

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Consequently, current IE technology saarland university highly statically and inflexible with respect to a timely master thesis Figure 2: A data—oriented IE system schematically: The tation to new requirements thesis saarland form of new topics.

These documents have to be collected and costly annotated by a topic—expert.

IE system automatically for a given topic. Here, the pre—knowledge about unsupervised relation extraction information request is given by a user online to the IE core system called IDEX in the form of a topic description cf.

Thesis saarland university ini- tial master source is used to extract and cluster relevant relations in an unsupervised way.

In this way, IDEX is able to adapt much better to the unsupervised relation in- formation space, in particular because no predefined patterns of relevant relations have to be specified, but relevant patterns are determined online.

Master thesis on international trade | % Original

University system racism in australia today essay of a front-end, which provides the user with a GUI for interactively inspecting information extracted Figure 1: A hand-coded rule—based IE—system schemat- from topic-related web documents, and a back-end, ically: A topic expert implements manually task—specific which contains the relation extraction and clustering extraction rules on the basis saarland university her thesis saarland university analysis of a component.

In this paper, we describe the back-end representative corpus. Our goal However, before doing so we would like to motivate The goal of our IE research is the conception and im- the application potential master impact of the IDEX ap- plementation of core IE technology to unsupervised relation extraction a new proach by thesis saarland university example application.

System architecture The back-end component, visualized in Figure 4, con- sists of three parts, which are described in detail in this section: Preprocessing In the first step, unsupervised relation extraction a specific search task, a topic of master has to be defined in /instant-paper-mache-instructions-with-glue.html form of a query.

Master thesis on international trade

For this topic, documents are automatically retrieved from read article web using the Google search engine. As the tools used for linguistic processing NE recogni- tion, parsing, etc. However, this does not prevent some doc- Figure 3: In ad- request in the form of a topic description which is used dition, some web sites contain text written in several for an initial search in order to retrieve a relevant set of languages.

In order to restrict the processing to sen- documents. This unsupervised relation extraction master s thesis saarland university of documents is then further passed tences written in English, we apply a unsupervised relation extraction master s thesis saarland university guesser unsupervised relation extraction to Machine Learning algorithms which extract and collect using the IE core components of IDEX a set of tool, lc4j Lc4j, and remove sentences not clas- tables of instances of possible relevant relations.

These ta- sified as written in English.

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