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| Content Provider | ACM Digital Library |
|---|---|
| Author | Nakamura, Taiga Iwama, Futoshi Takeuchi, Hironori |
| Abstract | This paper describes a novel framework for creating a parser to process and analyze texts written in a ``partially structured'' natural language. In many projects, the contents of document artifacts tend to be described as a mixture of formal parts (i.e. the text constructs follow specific conventions) and parts written in arbitrary free text. Formal parsers, typically defined and used to process a description with rigidly defined syntax such as program source code are very precise and efficient in processing the formal part, while parsers developed for natural language processing (NLP) are good at robustly interpreting the free-text part. Therefore, combining these parsers with different characteristics can allow for more flexible and practical processing of various project documents. Unfortunately, conventional approaches to constructing a parser from multiple small parsers were studied extensively only for formal language parsers and are not directly applicable to NLP parsers due to the differences in the way the input text is extracted and evaluated. We propose a method to configure and generate a combined parser by extending an approach based on parser combinator, the operators for composing multiple formal parsers, to support both NLP and formal parsers. The resulting text parser is based on Parsing Expression Grammars, and it benefits from the strength of both parser types. We demonstrate an application of such combined parser in practical situations and show that the proposed approach can efficiently construct a parser for analyzing project-specific industrial specification documents. . |
| Starting Page | 1012 |
| Ending Page | 1021 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781467310673 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-06-02 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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