ABSTRACT 

The purpose of this study is to propose a natural parallel cooperation model 
for natural language processing (NLP) by making use of the advantages of the 
Parallel Inference Machine. 

In recent years, system integration, including morphological analysis, syn-
tactic analysis and semantic analysis, has been proposed in the NLP field. 

As the basis of this proposal, it is recognized that information processing 
done by human has been carried out under partiality or incompleteness of 
information. Thus integrated NLP can adopt parallel processing because it 
disregards the processing direction. Therefore the integration of NLP and 
parallel cooperative processing can be regarded as a natural model. 

However, there are few implemented systems based on this model. It is 
because efficient parallel cooperation requires all processes to exchange all of 
their information with each other, but information exchange and its control is 
hard to implement. One solution to this problem is to abstract the processing 
framework so that analysis phases such as morphological analysis, syntactic 
analysis are carried out by one single processing mechanism. Our processing 
framework utilize type inferencing with respect to record-like type structure. 

APPROACH 

Some efficient algorithms already exist for morphological and syntactic pro-
cessing whose knowledge we should not ignore in developing a practical sys-
tem, even in the case of an integrated NLP system. 




Parsing base on layered stream method 

We have found that there is a strict correspondence between our verti-
cal type judgment and already established syntactic analysis methods. Mat-
sumoto's parallel parser PAX performs syntactic processing in parallel through 


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