Entities in .NET

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3.2.2. Entities
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The simplest and most reliable IE technolog is entity recognition, which we will abbreviate NE following the original Message Understanding Conference (MUC) de nitions (SAIC, 1998) NE systems identify all the names of people, places, organisations, dates, amounts of money, etc. All things being equal, NE recognition can be performed at up to around 95 % accuracy. Given that human annotators do not perform to the 100 % level (measured by inter-annotator comparisons), NE recognition can now be said to function at human performance levels, and applications of the technology are increasing rapidly as a result. The process is weakly domain-dependent, that is changing the subject matter of the texts being processed from nancial news to other types of news would involve some changes to the system, and changing from news to scienti c papers would involve quite large changes.
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3.2.3. Mentions
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Finding the mentions of entities involves using of coreference resolution (CO) to identify identity relations between entities in texts. These entities are both those identi ed by NE recognition and anaphoric references to those entities. For example, in:
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Alas, poor Yorick, I knew him Horatio .
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coreference resolution would tie Yorick with him (and I with Hamlet, if suf cient information was present in the surrounding text). This process is less relevant to end users than other IE tasks (i.e. whereas the other tasks produce output that is of obvious utility for the application user, this task is more relevant to the needs of the application developer). For text browsing purposes, we might use CO to highlight all occurrences of the same object or provide hypertext links between them. CO technology might also be used to make links between documents. The main signi cance of this task, however, is as a building block for TE and ST (see below). CO enables the association of descriptive information scattered across texts with the entities to which it refers. CO breaks down into two sub-problems: anaphoric resolution (e.g., I with Hamlet); proper-noun resolution. Proper-noun coreference identi cation nds occurences of same object represented with different
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SEMANTIC ANNOTATION AND HUMAN LANGUAGE TECHNOLOGY
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spelling or compounding, for example IBM , IBM Europe , International Business Machines Ltd , ). CO resolution is an imprecise process, particularly when applied to the solution of anaphoric reference. CO results vary widely; depending on domain perhaps only 50 60 % may be relied upon. CO systems are domain dependent.
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3.2.4. Descriptions
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The description extraction task builds on NE recognition and coreference resolution, associating descriptive information with the entities. To match the original MUC de nitions as before, we will abbreviate this task as TE . For example, in a news article the Bush administration can be also referred to as government of cials the TE task discovers this automatically and adds it as an alias. Good scores for TE systems are around 80 % (on similar tasks humans can achieve results in the mid 90s, so there is some way to go). As in NE recognition, the production of TEs is weakly domain dependent, that is changing the subject matter of the texts being processed from nancial news to other types of news would involve some changes to the system, and changing from news to scienti c papers would involve quite large changes.
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3.2.5. Relations
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As described in Appelt (1999), The template relation task requires the identi cation of a small number of possible relations between the template elements identi ed in the template element task. This might be, for example, an employee relationship between a person and a company, a family relationship between two persons, or a subsidiary relationship between two companies. Extraction of relations among entities is a central feature of almost any information extraction task, although the possibilities in real-world extraction tasks are endless . In general good template relation (TR) system scores reach around 75 %. TR is a weakly domain dependent task.
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