The Semantic Score Approach to the Disambiguation of PP Attachment Problem
Chao-Lin Liu, Jing-Shin Chang, Keh‐Yih Su · 1990
In a Natural Language Processing System which takes English as the source input lan-guage, the syntactic roles of the prepositional phrases in a sentence are difficult to identify. A large number of ambiguities may result from these phrases. Traditional rule-based approaches to this problem rely heavily on general linguistic knowledge, complicated knowledge bases and sophisticated control mechanism. When uncertainty about the attachment patterns is en-countered, some heuristics and ad hoc procedures are adopted to assign attachment preference for disambiguation. Hence, although the literatures about this topic are abundant, there is no guarantee of the objectiveness and optimality of these approaches. In this paper, a probabilistic semantic model is proposed to resolve the PP attachment problem without using complicated knowledge bases and control mechanism. This approach elegantly integrates the linguistic model for semantics interpretation and the objective char-acteristics of the probabilistic Semantic Score model. Hence, it will assign a much more objective preference measure to each ambiguous attachment pattern. It is found that approx-imately 90 % of the PP attachment problem in computer manuals can be solved with this