Definitions Of Referential Ambiguity

i. Identify Lexical Ambiguity

The Lexical database contains the possible ambiguity indicators such as: bound, break, call, content, continue, contract, count, direct, even, express, form, forward, function, get, job, level, name, notice, number, out, part, position, record, reference, return, set, source, special, standard, string, subject, switch, tail, throw, throw, throw, translate, try, under, value , way.

ii. Identify Referential Ambiguity

The Referential database contains the possible ambiguity indicators such as: anybody, anyone, anything, everybody, everyone, everything, he, her, hers, herself, him, himself, his, I, it, its, itself, me, our, ours, ourselves, she, somebody, someone, something, that, their, theirs, them, themselves,

For each ambiguous sentence, resolve the ambiguity in the sentence automatically as the final step using resolving rules, and thus improve the natural language requirement specification document. The Ambiguity Resolving Module architecture is shown in Figure 4. Figure 4 The Ambiguity Resolving Module

The resolving ambiguity approach uses the following common rules to check if a sentence contains an ambiguity:

Rule 1: when sentence containing not only, but also, as well as, both, but, and, and also, or, and/or, X /Y, either, whether, otherwise, meanwhile, whereas, on the other hand split it to two sentences.

Rule 2: when sentence containing unless, replace with if not.

Rule 3: when sentence containing a, an, all, any, some, every, several replace with each.

Rule 4: when sentence containing should, will, would, may, might, ought to replace with shall.

Rule 5: when sentence containing There is X in Y, X exists in Y replace with Y has X.

Rule 6: when sentence containing anaphora or pronoun such as they or them replaces with the farthest

OpenNLP is an open source Java library which is used process Natural Language text. OpenNLP provides services such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and co-reference resolution, etc. The OpenNLP library was used to build an efficient text processing service. In this section the screenshots of DARA is provided. The graphical user interface (GUI) is shown to aid in the description. The GUI when the tool is in the run state is shown in Figure 5. The DARA GUI is composed of four principal