TACITUS Project
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A TACITUS Project is a Research Project developed at SRI International that aims to investigate the usage of knowledge in Discourse Interpretation.
- Context:
- It resulted in the creation of the TACITUS System.
- …
- Counter-Example(s):
- See: Abductive Inference, Knowledge Base, Domain Knowledge.
References
1988
- (Hobbs et al., 1988) ⇒ Jerry R. Hobbs, Mark Stickel, Paul Martin, and Douglas Edwards. (1988). “Interpretation as Abduction". In: Proceedings of the 26th annual meeting on Association for Computational Linguistics (ACL 1988). DOI:10.3115/982023.982035
- QUOTE: Abductive inference is inference to the best explanation. The process of interpreting sentences in discourse can be viewed as the process of providing the best explanation of why the sentences would be true. In the TACITUS Project at SRI, we have developed a scheme for abductive inference that yields a significant simplification in the description of such interpretation processes and a significant extension of the range of phenomena that can be captured. It has been implemented in the TACITUS System (Stickel, 1982; Hobbs, 1986; Hobbs and Martin, 1987) and has been and is being used to solve a variety of interpretation problems in casualty reports, which are messages about breakdowns in machinery, as well as in other texts[1].
It is well-known that people understand discourse so well because they know so much. Accordingly, the aim of the TACITUS Project has been to investigate how knowledge is used in the interpretation of discourse. This has involved building a large knowledge base of commonsense and domain knowledge (see Hobbs et al., 1986), and developing procedures for using this knowledge for the interpretation of discourse.
- QUOTE: Abductive inference is inference to the best explanation. The process of interpreting sentences in discourse can be viewed as the process of providing the best explanation of why the sentences would be true. In the TACITUS Project at SRI, we have developed a scheme for abductive inference that yields a significant simplification in the description of such interpretation processes and a significant extension of the range of phenomena that can be captured. It has been implemented in the TACITUS System (Stickel, 1982; Hobbs, 1986; Hobbs and Martin, 1987) and has been and is being used to solve a variety of interpretation problems in casualty reports, which are messages about breakdowns in machinery, as well as in other texts[1].
- ↑ Charniak (1986) and Norvig (1987) have also applied abductive inference techniques to discourse interpretation.
1987a
- (Hobbs & Martin, 1987) ⇒ Jerry R. Hobbs, and Paul Martin (1987). "Local Pragmatics". In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 1987).
- QUOTE: The TACITUS system is intended to embody that theory. Its specific aim is the interpretation of casualty reports (casreps), which are messages in free-flowing text about breakdowns in mechanical devices. More broadly, however, our aim is to develop general procedures, together with the underlying theory, for using commonsense and technical knowledge in the interpretation of written (and spoken) discourse regardless of domain.
1987b
- (Norvig, 1987) ⇒ Peter Norvig (1987). "Inference in Text Understanding". In: Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI-87).
1986a
- (Hobbs, 1986) ⇒ Jerry R. Hobbs (1986). "Overview of the TACITUS Project". In: Proceeding of the Strategic Computing Natural Language Workshop (HLT 1986).
- QUOTE: The specific aim of the TACITUS project is to develop interpretation processes for handling casualty reports (casreps), which are messages in free-flowing text about breakdowns of machinery[1]. These interpretation processes will be an essential component, and indeed the principal component, of systems for automatic message routing and systems for the automatic extraction of information from messages for entry into a data base or an expert system. In the latter application, for example, it is desirable to be able to recognize conditions in the message that instantiate conditions in the antecedents of the expert system's rules, so that the expert system can reason on the basis of more up-to-date and more specific information.
More broadly, our aim is to develop general procedures, together with the underlying theory, for using commonsense and technical knowledge in the interpretation of written discourse. This effort divides into five subareas: (1) syntax and semantic translation; (2) commonsense knowledge; (3) domain knowledge; (4) deduction; (5) "local" pragmatics. Our approach in each of these areas is discussed in turn.
- QUOTE: The specific aim of the TACITUS project is to develop interpretation processes for handling casualty reports (casreps), which are messages in free-flowing text about breakdowns of machinery[1]. These interpretation processes will be an essential component, and indeed the principal component, of systems for automatic message routing and systems for the automatic extraction of information from messages for entry into a data base or an expert system. In the latter application, for example, it is desirable to be able to recognize conditions in the message that instantiate conditions in the antecedents of the expert system's rules, so that the expert system can reason on the basis of more up-to-date and more specific information.
- ↑ The TACITUS project is funded by the Defense Advanced Research Projects Agency under Office of Naval Research contract N00014-85-C-0013, as part of the Strategic Computing program.
1986b
- (Charniak, 1986) ⇒ Eugene Charniak (1986). "A Neat Theory of Marker Passing". In: Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86).
- QUOTE: This paper describes Wimp (Wholy Integrated Marker Passer), a program which understands simple stories in English. Wimp uses incoming words (in particular the open-class words) as input, to a marker passer which finds connections between these words. These connections, or paths go to a path checker which makes sure that the paths “make sense” and extracts from them the facts which are needed to plausibly claim that the input has been “understood”.
1982
- (Stickel, 1982) ⇒ Mark E. Stickel (1982). "A Non-clausal Connection-Graph Theorem-Proving Program". In: Proceedings of the National Conference on Artificial Intelligence (2nd AAAI 1982).
- QUOTE: This paper describes some of the theory and features of a non-clausal connection-graph resolution theorem-proving program being developed as a reasoning component of a natural language-understanding system.