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phase annotating all possible mentions of instances from the TAP ontology. In the second, disambiguation phase, SemTag uses a vectorspace model to assign the correct ontological class or determine that this mention does not correspond to a class in TAP. The disambiguation is carried out by comparing the context of the current mention with the contexts of instances in TAP with compatible aliases, using a window of 10 words either side of the mention. The TAP ontology, which contains about 65,000 instances, is very similar in size and structure to the KIM Ontology and instance base discussed in Section 5.5. (e.g., each instance has a number of lexical aliases). One important characteristic of both ontologies is that they are very lightweight and encode only essential properties of concepts and instances. In other words, the goal is to cover frequent, commonly known and searched for instances (e.g., capital cities, names of presidents), rather than to encode an extensive set of axioms enabling deep, Cyc-style reasoning. As reported by (Mahesh et al., 1996), the heavyweight logical approach undertaken in Cyc is not appropriate for many NLP tasks. The SemTag system is based on a high-performance parallel architecture -Seeker, where each node annotates about 200 documents per second. The demand for such parallelism comes from the big volumes of data that need to be dealt with in many applications and make automatic semantic annotation the only feasible option. A parallel architecture of a similar kind is currently under development for KIM and, in general, it is an important ingredient of large-scale automatic annotation approaches.





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Compared to CB, GCB generally requires more client buffer because all but the rst media segments in a Type-II group are not transmitted in a just-in-time manner. Instead, they are transmitted at a higher rate so that reception can be completed at the same time as the rst media segment. Consequently, these media segments are received completely before the playback time and thus occupy more client buffer. Speci cally, the client will play back media segment L i at time t0 + (m + i) U , where t0 is the time the client entered the system. We de ne Hi as the amount of media data received but not yet played back at time ti as de ned in (18.14). As channel switching occurs only at the time instants t0 + (m + h j ) U for j = 0, 1, . . . , we only need to consider the buffer occupancy at these instants. We can compute Hh j recursively from

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The Knowledge and Information Management system (KIM) is a product of OntoText Lab which is currently being used and further developed in SEKT (Kiryakov et al., 2005). KIM is an extensible platform for semantics-based knowledge management which offers IE-based facilities for metadata creation, storage and conceptual search. The system has a server-based core that performs ontology-based IE and stores results in a central knowledge base. This server platform can then be used by diverse applications as a service for annotating and querying document spaces. The ontology-based Information Extraction in KIM produces annotations linked both to the ontological class and to the exact individual in the instance base. For new (previously unknown) entities, new identi ers are allocated and assigned; then minimal descriptions are added to the semantic repository. The annotations are kept separately from the content, and an API for their management is provided.

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