Recent work on joint word segmentation, POS (Part Of Speech) tagging, and dependency parsing in Chinese has two key problems: the first is that word segmentation based on character and dependency parsing based on word were not combined well in the transition-based framework, and the second is that the joint model suffers from the insufficiency of annotated corpus. In order to resolve the first problem, we propose to transform the traditional word-based dependency tree into character-based dependency tree by using the internal structure of words and then propose a novel character-level joint model for the three tasks. In order to resolve the second problem, we propose a novel semi-supervised joint model for exploiting n-gram feature and dependency subtree feature from partially-annotated corpus. Experimental results on the Chinese Treebank show that our joint model achieved 98.31%, 94.84% and 81.71% for Chinese word segmentation, POS tagging, and dependency parsing, respectively. Our model outperforms the pipeline model of the three tasks by 0.92%, 1.77% and 3.95%, respectively. Particularly, the F1 value of word segmentation and POS tagging achieved the best result compared with those reported until now.

This paper describes methods in which natural language is used to describe video contents, knowledge of which is needed for intelligent video manipulation. The content encoded by natural language is extracted by a language analyzer in the form of subject-centered dependency structures which is a language-oriented structure, and is combined in an incremental way into a single structure called a multi-path index tree. Content descriptors and their inter-relations are extracted from the index tree in order to provide a high speed retrieval and flexibility. The content-based video index is represented in a two-dimensional structure where in the descriptors are mapped onto a component axis and temporal references (i.e., video segments aligned to the descriptors) are mapped onto a time axis. We implemented an experimental image retrieval systems to illustrate the proposed index structure 1) has superior retrieval capabilities compare to those used in conventional methods, 2) can be generated by an automated procedure, and 3) has a compact and flexible structure that is easily expandable, making an integration with vision processing possible.

In this paper, we present a new technique for the semantic analysis of sentences, including an ambiguity-packing method that generates a packed representation of individual syntactic and semantic structures. This representation is based on a dependency structure with constraints that must be satisfied in the syntax-semantics mapping phase. Complete syntax-semantics mapping is not performed until all ambiguities have been resolved, thus avoiding the combinatorial explosions that sometimes occur when unpacking locally packed ambiguities. A constraint satisfaction technique makes it possible to resolve ambiguities efficiently without unpacking. Disambiguation is the process of applying syntactic and semantic constraints to the possible candidate solutions (such as modifiees, cases, and wordsenses) and removing unsatisfactory condidates. Since several candidates often remain after applying constraints, another kind of knowledge to enable selection of the most plausible candidate solution is required. We call this new knowledge a preference. Both constraints and preferences must be applied to coordination for disambiguation. Either of them alone is insufficient for the purpose, and the interactions between them are important. We also present an algorithm for controlling the interaction between the constraints and the preferences in the disambiguation process. By allowing the preferences to control the application of the constraints, ambiguities can be efficiently resolved, thus avoiding combinatorial explosions.