PostgreSQL分析器的研究

介绍了对象关系数据库系统 ＰｏｓｔｇｒｅＳＱＬ的内部结构、查询处理的过程及 ＰｏｓｔｇｒｅＳＱＬ语法／词法分析器的实现，并通过一个 ＳＱＬ语句实例，分析了 ＰｏｓｔｇｒｅＳＱＬ语法／词法分析器的内部结构，最后给出Ｑｕｅｒｙ树的详细数据结构图示．

Construction method of Chinese sentential semantic structure

A new method is proposed for constructing the Chinese sentential semantic structure in this paper. The method adopts the features including predicates, relations between predicates and basic arguments, relations between words, and case types to train the models of CRF ＋ ＋ and de- pendency parser. On the basis of the data set in Beijing Forest Studio-Chinese Tagged Corpus （ BFS- CTC）, the proposed method obtains precision value of 73.63% in open test. This result shows that the formalized computer processing can construct the sentential semantic structure absolutely. The features of predicates, topic and comment extracted with the method can be applied in Chinese in- formation processing directly for promoting the development of Chinese semantic analysis. The method makes the analysis of sentential semantic analysis based on large scale of data possible. It is a tool for expanding the corpus and has certain theoretical research and practical application value.

Discussion on Individual Expected Maturity Age of Populus tomentosa Carr

Through the study of parse wood materials in Shandong Province, the fitting empirical equation of tree growth was obtained, a function with tree growth was a variable and time as an independent variable. Through mathematical operations such as function derivation, the mature age of tree growth was obtained. The obtained expected maturity age for Populus tomentosa forest was 11 a for pulp material, 26 a for pillar materials and 41 a for peeler. And the application, research directions and precautions of the mature ages were proposed.

Study on Mature Age of Individual Larix kaempferi

Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such as function derivation,the mature age of tree growth was obtained,and the obtained expected mature age for Larix kaempferi was 46 years.And the application,research directions and precautions of the mature ages were proposed.

Study on Mature Age of an Individual Quercus acutissima

Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such as function derivation,the mature age of tree growth was obtained,and the obtained mature age for the actual forest of Quercus acutissima was 66a.And the application,research directions and precautions of the mature ages were proposed.

Study on Expected Mature Age of Individual Saliz matsudana

Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such as function derivation,the mature age of tree growth was obtained,and the obtained expected mature age of Saliz matsudana was 21a.And the application,research directions and precautions of the mature ages were proposed.

Discussion on Individual Expected Maturity of Ulmus pumila

The study on the parse wood got the fitting empirical equation of tree growth, in which the tree growth rate was used as the variable and time as the in- dependent variable. The arithmetical operation to the function got the maturity age of tree growth, and the expected maturity of Ulmus pumila was 21 a. In addition, the application as well as the research direction and matters needing attention were proposed.

Study on Mature Age of An Individual Pinus thunbergii Parl

Through the study of parse wood materials, the fitting empirical equation of tree growth was obtained, a function with tree growth as a variable and time as an independent variable. Through mathematical operations such as function derivation, the mature age of tree growth was obtained. The obtained maturity age for the actual forest of Pinus thunbergii Parl was 51 a, and the ideal mature age was 66 a. And the application, research directions and precautions of the mature ages were proposed.

Study on Mature Age of An Individual Robinia pseudoacacia

Through the study of parse wood materials, the fitting empirical equation of tree growth was obtained, a function with tree growth as a variable and time as an independent variable. The mature age of tree growth was obtained through mathematical operations such as function derivation. The obtained expected maturity ages for the actual forests of Robinia pseudoacacia were 36, 46 and 56 a, respectively, which could be the mature ages for commercial forest, protection forest and special-purpose forest. And the application, research directions and precautions of the mature ages were proposed.

Discussion on Individual Expected Maturity of Populus canadensis Moench

Through the study of parse wood materials in Shandong Province, the fitting empirical equation of tree growth was obtained, a function with tree growth was a variable and time as an independent variable. Through mathematical operations such as function derivation, the mature age of tree growth was obtained. The obtained expected maturity age for Populus canadensis Moench forest was 11 a for pulp material, 26 a for pillar materials and 41 a for peeler. And the application, research directions and precautions of the mature ages were proposed.

Effects of Light on Tree Growth

Empirical equations were used to fit tree growth through the analysis of parse wood data, functions with tree growth amount as the dependent variable and annual sunshine duration as the independent variable. According to arithmetical operations like derivation of the functions, the relative contribution rate of light to tree growth was 64.8%, which was almost equal to the relative contribution rate of precipitation to tree growth. Therefore, the light and precipitation were of equal importance to tree growth.

Kernel-Based Semantic Relation Detection and Classification via Enriched Parse Tree Structure

This paper proposes a tree kernel method of semantic relation detection and classification （RDC） between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.

Semantic Role Labeling of Chinese Nominal Predicates with Dependency-Driven Constituent Parse Tree Structure

This paper explores a tree kernel based method for semantic role labeling （SRL） of Chinese nominal predicates via a convolution tree kernel. In particular, a new parse tree representation structure, called dependency-driven constituent parse tree （D-CPT）, is proposed to combine the advantages of both constituent and dependence parse trees. This is achieved by directly representing various kinds of dependency relations in a CPT-style structure, which employs dependency relation types instead of phrase labels in CPT （Constituent Parse Tree）. In this way, D-CPT not only keeps the dependency relationship information in the dependency parse tree （DPT） structure but also retains the basic hierarchical structure of CPT style. Moreover, several schemes are designed to extract various kinds of necessary information, such as the shortest path between the nominal predicate and the argument candidate, the support verb of the nominal predicate and the head argument modified by the argument candidate, from D-CPT. This largely reduces the noisy information inherent in D-CPT. Finally, a convolution tree kernel is employed to compute the similarity between two parse trees. Besides, we also implement a feature-based method based on D-CPT. Evaluation on Chinese NomBank corpus shows that our tree kernel based method on D-CPT performs significantly better than other tree kernel-based ones and achieves comparable performance with the state-of-the-art feature-based ones. This indicates the effectiveness of the novel D-CPT structure in representing various kinds of dependency relations in a CPT-style structure and our tree kernel based method in exploring the novel D-CPT structure. This also illustrates that the kernel-based methods are competitive and they are complementary with the feature- based methods on SRL.

Linear algorithm for lexicographic enumeration of CFG parse trees

We study CFG parse tree enumeration in this paper. By dividing the set of all parse trees into infinite hierarchies according to height of parse tree, the hierarchical lexicographic order on the set of parse trees is established. Then grammar-based algorithms for counting and enumerating CFG parse trees in this order are presented. To generate a parse tree of height n, the time complexity is O（n）. If τ is a lowest parse tree for its yield, then O（n） =O（｜｜τ｜｜ ＋ 1）, where ｜｜τ｜｜ is the length of the sentence （yield） generated by τ. The sentence can be obtained as a by-product of the parse tree. To compute sentence from its parse tree （needn＇t be lowest one）, the time complexity is O（node）＋O（｜｜τ｜｜ ＋ 1）, where node is the number of non-leaf nodes of parse tree τ. To generate both a complete lowest parse tree and its yield at the same time, the time complexity is O（｜｜τ｜｜ ＋ 1）.

Neural Parse Combination

Analyzing the syntactic structure of natural languages by parsing is an important task in artificial intelligence. Due to the complexity of natural languages, individual parsers tend to make different yet complementary errors. We propose a neural network based approach to combine parses from different parsers to yield a more accurate parse than individual ones. Unlike conventional approaches, our method directly transforms linearized candidate parses into the ground-truth parse. Experiments on the Penn English Treebank show that the proposed method improves over a state-of-the-art parser combination approach significantly.