Many systems have been developed for creating syntactically annotated corpora. However, they mainly focus on interface usability and hardly pay attention toknowledge sharing among annotators in the task. In order to incorporate the functionality of knowledge sharing, we emphasized the importance of normalizingthe annotation process. As a first step toward knowledge sharing, this paper proposes a method of system initiative annotation in which the system suggests annotators the order of ambiguities to solve. To be more concrete, the system forces annotators to solve ambiguity of constituent structure in a top-down and depth-first manner, and then to solve ambiguity of grammatical category in a bottom-up and breadth-first manner. We implemented the system on top of eBonsai, our annotation tool, and conducted experiments to compare eBonsai and the proposed system in terms of annotation accuracy and efficiency. We found that at least for novice annotators, the proposed system is more efficient while keeping annotation accuracy comparable with eBonsai.

This paper introduces a tool \Bonsai which supports human in annotating corpora with morphosyntactic information, and in retrieving syntactic structures stored in the database. Integrating annotation and retrieval enables users to annotate a new instance while looking back at the already annotated sentences which share the similar morphosyntactic structure. We focus on the retrieval part of the system, and describe a method to decompose a large input query into smaller ones in order to gain retrieval efficiency. The proposed method is evaluated with the Penn Treebank corpus, showing significant improvements.

In Japanese constructions of the form [N1 no Adj N2], the adjective Adj modifies either N1 or N2. Determing the semantic dependencies of adjective in such phrase is an important task for machine translation. This paper describes a method for determining the adjective dependency in such constructions using decision lists, and inducing decision lists from training contexts with correct semantic dependencies and without. Based on evaluation, our method is able to determine adjective dependency with an precision of about 94%. We further analyze rules in the induced decision lists and examine effective features to determine the semantic dependencies of adjectives.