SUBDIVIDING VERBS TO IMPROVE SYNTACTIC PARSING

This paper proposes a new way to improve the performance of dependency parser: subdividing verbs according to their grammatical functions and integrating the information of verb subclasses into lexicalized parsing model. Firstly,the scheme of verb subdivision is described. Secondly,a maximum entropy model is presented to distinguish verb subclasses. Finally,a statistical parser is developed to evaluate the verb subdivision. Experimental results indicate that the use of verb subclasses has a good influence on parsing performance.

Fast Chinese syntactic parsing method based on conditional random fields

A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses the bottom-up to connect the recognized phrase nodes to construct the syn- tactic tree. On the basis of Beijing forest studio Chinese tagged corpus, two experiments are de- signed to select the training parameters and verify the validity of the method. The result shows that the method costs 78. 98 ms and 4. 63 ms to train and test a Chinese sentence of 17. 9 words. The method is a new way to parse the phrase structure grammar for Chinese, and has good generalization ability and fast speed.

Improving Syntactic Parsing of Chinese with Empty Element Recovery

This paper puts forward and explores the problem of empty element （EE） recovery in Chinese from the syntactic parsing perspective, which has been largely ignored in the literature. First, we demonstrate why EEs play a critical role in syntactic parsing of Chinese and how EEs can better benefit syntactic parsing of Chinese via re-categorization from the syntactic perspective. Then, we propose two ways to automatically recover EEs： a joint constituent parsing approach and a chunk-based dependency parsing approach. Evaluation on the Chinese TreeBank （CTB） 5.1 corpus shows that integrating EE recovery into the Charniak parser achieves a significant performance improvement of 1.29 in Fl-measure. To the best of our knowledge, this is the first close examination of EEs in syntactic parsing of Chinese, which deserves more attention in the future with regard to its specific importance.

Using SVM to construct a Chinese dependency parser

In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.

Chinese spoken language understanding in SHTQS

Spoken dialogue systems are an active research field with wide applications. But the differences in the Chinese spoken dialogue system are not as distinct as that of English. In Chinese spoken dialogues, there are many language phenomena. Firstly, most utterances are ill-formed. Secondly, ellipsis, anaphora and negation are also widely used in Chinese spoken dialogue. Determining how to extract semantic information from incomplete sentences and resolve negation, anaphora and ellipsis is crucial. SHTQS (Shanghai Transportation Query System) is an intelligent telephone-based spoken dialogue system providing information about the best route between any two sites in Shanghai. After a brief description of the system, the natural language processing is emphasized. Speech recognition sentences unavoidably contain errors. In language sequence processing procedures, these errors can be easily passed to the later parts and take on a ripple effect. To detect and recover these from errors as early as possible, language-processing strategies are specially considered. For errors resulting from divided words in speech recognition, segmentation and POS Tagging approaches that can rectify these errors are designed. Since most of the inquiry utterances are ill-formed and negation, anaphora and ellipsis are common language phenomena, the language understanding must be adequately adaptive. So, a partial syntactic parsing scheme is adopted and a chart algorithm is used. The parser is based on unification grammar. The semantic frame that extracts from the best arc set of the chart is used to represent the meaning of sentences. The negation, anaphora and ellipsis are also analyzed and corresponding processing approaches are presented. The accuracy of the language processing part is 88.39% and the testing result shows that the language processing strategies are rational and effective.

Semantic Entity Recognition and Relation Construction Method for Assembly Process Document

Assembly process documents record the designers'intention or knowledge.However,common knowl-edge extraction methods are not well suitable for assembly process documents,because of its tabular form and unstructured natural language texts.In this paper,an assembly semantic entity recognition and relation con-struction method oriented to assembly process documents is proposed.First,the assembly process sentences are extracted from the table through concerned region recognition and cell division,and they will be stored as a key-value object file.Then,the semantic entities in the sentence are identified through the sequence tagging model based on the specific attention mechanism for assembly operation type.The syntactic rules are designed for realizing automatic construction of relation between entities.Finally,by using the self-constructed corpus,it is proved that the sequence tagging model in the proposed method performs better than the mainstream named entity recognition model when handling assembly process design language.The effectiveness of the proposed method is also analyzed through the simulation experiment in the small-scale real scene,compared with manual method.The results show that the proposed method can help designers accumulate knowledge automatically and efficiently.

Weak Precedence Story Parsing Grammar

Story understanding is one of the important branches of natural languageunderstanding research in AI techlliques. The story understanding approachbased on Story Parsing Grammar (SPG) involves that SPG is used to rep-resent different abstracting processes of stories with different levels in storyunderstanding and that the story understanding process is converted to therecognition process of stories using the syntactic parser of SPG. This kind ofstory understanding is cal1ed story parsing. In this paper, firstly a subclassof SPG, called Weak Precedence SPG (WPSPG), is defined. Afterwards thesyntactic parsing algorithm of WPSPG is studied. An example of story parsingis also given.