“…In a previous study, the authors described a system for the translation of inorganic nomenclatures (IUPAC, Stock, and conventional) and of the trivial names of inorganic chemicals. This system incorporates an error handler in which the various types of errors are classified by severity and the stage at which they are detected; the translator is then informed of their presence.…”
Section: Discussionmentioning
confidence: 99%
“…In recent years, this research has been focussed on the development of report generators which will produce consistent, flexible messages in sentences close to the user's own language. This research is closely connected with work on explanatory systems in “intelligent” tutorials. ,,, …”
The treatment of errors generated in the transmission of chemical information from humans to machines is examined. Every type of human-machine communication requires a translation subprocess to deal with the various possible representations of knowledge; the present study was designed to consider errors occurring in such a subsystem. Regardless of the model employed, translation of knowledge is performed over several successive stages; at each stage different types of errors may be detected. A method is proposed to classify these errors according to the stage at which they occur, thus facilitating the generalization of the process. An analysis of errors occurring during the translation of inorganic chemical names is also presented. The particular grammatical features of different nomenclatures would require a study of the errors appearing in the human-machine communication process in modern chemistry-oriented systems, a problem which has only been touched upon in the systems proposed to date. Attention here is centered on lexicographic errors, since that stage of the translation process is completely independent of the model employed and might thus be useful in a more general sense.
“…In a previous study, the authors described a system for the translation of inorganic nomenclatures (IUPAC, Stock, and conventional) and of the trivial names of inorganic chemicals. This system incorporates an error handler in which the various types of errors are classified by severity and the stage at which they are detected; the translator is then informed of their presence.…”
Section: Discussionmentioning
confidence: 99%
“…In recent years, this research has been focussed on the development of report generators which will produce consistent, flexible messages in sentences close to the user's own language. This research is closely connected with work on explanatory systems in “intelligent” tutorials. ,,, …”
The treatment of errors generated in the transmission of chemical information from humans to machines is examined. Every type of human-machine communication requires a translation subprocess to deal with the various possible representations of knowledge; the present study was designed to consider errors occurring in such a subsystem. Regardless of the model employed, translation of knowledge is performed over several successive stages; at each stage different types of errors may be detected. A method is proposed to classify these errors according to the stage at which they occur, thus facilitating the generalization of the process. An analysis of errors occurring during the translation of inorganic chemical names is also presented. The particular grammatical features of different nomenclatures would require a study of the errors appearing in the human-machine communication process in modern chemistry-oriented systems, a problem which has only been touched upon in the systems proposed to date. Attention here is centered on lexicographic errors, since that stage of the translation process is completely independent of the model employed and might thus be useful in a more general sense.
“…Figure shows how the error handling system is called by the lexical analyzer once its analysis process has been concluded. The system is incorporated into the inorganic nomenclature translator system, and operates as follows: 1 Error handler subsystem (DIMS (dynamic internal representation of molecular structures) and CST (compound structure table)).…”
Section: The Error Handler Subsystemmentioning
confidence: 99%
“…•The error handler attempts to repair errors discovered by the lexical analyzer at each successive step, Scan-1, Scan-2, and Scan-3 …”
Section: The Error Handler Subsystemmentioning
confidence: 99%
“…A general approach to deal with these factors has been proposed. , However, since syntactic compatibility of the repair depends on the grammer of the language involved, a different solution is required for each language. This article describes how the proposed model was applied to the repair of lexical errors detected during the translation of inorganic names to an abstract data structure by a translation system described in previous studies (see Figure ). , …”
The development of a system for the repair of lexical errors
detected in the process of recognizing inorganic
chemical names is described. Repairs are based on the calculation
of similarity between strings, using the
model and approximate matching algorithm detailed in the previous
article of this series. A hierarchical
data structure is proposed for the storage of information generated
during the repair process. This structure
comprises two hierarchically-related abstract data types, to permit the
storage of partial and total repairs
generated by the process. The great amount of information
generated by the system might recommend its
use in applications requiring explanatory subsystems to report on the
success of recognition of user-input
information. Problems in the repair process arising from inorganic
grammar structures are also examined,
and solutions are proposed for cases such as that of the delimiters
between significant parts of the inorganic
substance name.
A model for the completion and balancing of inconsistent inorganic reactions is presented. A series of fuzzy parameters is proposed. These parameters are considered within a knowledge frame representing inorganic reactions via a semantic/functional network and, through the calculation of possibility measures, allow the completion and solution of such reactions by considering new inorganic species to be added to the reaction. Species to be added are formulated on the basis of atoms present and/or absent in the reaction, along with valence, system features, and a series of flags that determines the cardinality of the possible solution set. Incorporation of the proposed model into an inorganic chemistry formulation system would contribute to the development of powerful computer-assisted learning systems, given the extent of information extracted from a reaction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
