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A neurocomputational model based on emergent massively overlapping neural cell assemblies (CAs) for resolving prepositional phrase (PP) attachment ambiguity is described. PP attachment ambiguity is a well-studied task in natural language processing and is a case where semantics is used to determine the syntactic structure. A large network of biolog...
Contexts in source publication
Context 1
... canonical example is illustrated in Figure 1, where Figure 1A shows the PP attached to the VP, with the telescope used as the instrument for seeing. Figure 1B shows the case where the PP attaches to the noun. ...
Context 2
... canonical example is illustrated in Figure 1, where Figure 1A shows the PP attached to the VP, with the telescope used as the instrument for seeing. Figure 1B shows the case where the PP attaches to the noun. ...
Context 3
... canonical example is illustrated in Figure 1, where Figure 1A shows the PP attached to the VP, with the telescope used as the instrument for seeing. Figure 1B shows the case where the PP attaches to the noun. This illustration based on syntax trees is consistent with most current syntactic theories (Jackendoff, 2002). ...
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Citations
... In this model the assumed context of a definition of ambiguity is that of linguistic ambiguity. Linguistic ambiguity is researched in the fields of linguistics, computational linguistics [32][33][34][35][36], and philosophy. to avoid having to quote them. ...
Despite a large amount of research in methods and tools for avoiding and detecting requirements ambiguity, recent studies have indicated that requirements ambiguity seems to be resolved through multiple inspections and discussions that characterize the requirements engineering process. However, this process may not catch ambiguity types that are likely to result in subconscious disambiguation. People are likely unaware of and incapable of recognizing these ambiguity types; therefore, these types are likely to remain after multiple inspections. This kind of ambiguity is defined as persistent ambiguity and may cause expensive damage. The potential impact of persistent ambiguity was investigated. Initially, a comprehensive ambiguity model based on linguistic ambiguity and its application to requirements engineering was developed. The model was subsequently analyzed to determine the ambiguity types likely to result in subconscious disambiguation and therefore likely to persist. Three requirements specifications were inspected for instances of persistent ambiguity as defined in the model. Each chief requirements engineer verified whether the persistent ambiguities likely to have the greatest impact on each project were indeed interpreted ambiguously, and if so, what the impact was. For the three requirements specifications inspected, there is an average of one persistent ambiguity for every 15.38 pages; project one has the highest average of one persistent ambiguity for every 3.33 pages, project three has an average of one persistent ambiguity for every 31.25 pages, and project two has the lowest average of one persistent ambiguity for every 56 pages. For the three projects, none of the persistent ambiguities reviewed by each chief requirements engineer caused expensive damage because all of the requirements engineers seemed to subconsciously disambiguate the ambiguities in the same way. For the three projects analyzed and the ambiguities reviewed by each chief requirements engineer, the least expensive approach would have been to forego initially identifying persistent ambiguity in these three projects. The first main conclusion is that persistent ambiguity remained undetected by the teams of requirements engineers. The second main conclusion is that the process used by these particular requirements engineering teams for these particular projects is enough to prevent damage. The third main conclusion is that the identification of persistent ambiguity in requirements specifications is potentially an effective and efficient strategy for minimizing damage caused by ambiguity precisely because of its focus on ambiguity that remained undetected due to lack of awareness. Further study is necessary to determine what factors are involved in persistent ambiguity and its prevalence, as well as its potential impacts.
... Their accuracy was 81.8%. The neural network by Nadh and Huyck (2012) also used WordNet word sense hierarchies. Only the first (intended to be the most frequent) sense of the word was used in computations. ...
