Shallow parsing

About: Shallow parsing is a research topic. Over the lifetime, 397 publications have been published within this topic receiving 10211 citations.

Morphosyntactic disambiguation and shallow parsing in computational processing of Basque

Abstract: Our goal in this article is to show the improvements in the computational treatment of Basque, and more specifically, in the areas of morphosyntactic disambiguation and shallow parsing The improvements presented in this paper include the following: analyses of previously identified ambiguities in morphosyntax and in syntactic functions, their disambiguation, and finally, an outline of possible steps in terms ofshallow parsing based on the results provided by the disambiguation process The work is part of the current research within the field of Natural Language Processing (NLP) in Basque, and more specifically, part of the work that is being done within the IXA group

Chinese Sentence Parsing Based on Maximum Entropy Model

Abstract: The shallow parsing theory is applied to partition Chinese sentence parsing into three procedures:TAG,CHUNK,BUILD and CHECK.To resolve the problem of lacking feature types for available probabilistic models and make the best of useful information for parsing in context,we present probabilistic model based on maximum entropy to evaluate the probability of each action in the parsing procedures.In this model,any useful information for parsing in a context could be an actual feature; the features and training events are defined; the strategy of feature selection and the algorithm of parameter estimation based on Generalized Iterative Scaling(GIS)are given; The final result of parsing is the parse tree with the largest probability searched with Breadth-first search(BFS).The model is experimentally proved satisfying in both parsing efficiency and precision.

Automatic extraction of abbreviation definitions based on general texts

Abstract: The study of abbreviation identifications mostly is limited to the biomedical literature. The wide use of abbreviations in general texts, including web data and newswire data, requires us to process and extract the abbreviation definition. In this paper, we propose an abbreviation definition identification algorithm, which employs a variety of rules and incorporates shallow parsing of the text to identify the most probable abbreviation definition from general texts. The performance of our system was tested with data set provided by 2012 NIST1 TAC-KBP2, obtaining a performance of 94.2% recall and 95.5% precision.

Discussion on the Integration of Statistical Learning Method and Artificial Rule Method for Prepositional Phrase Recognition

Abstract: In recognition of prepositional phrases,statistical learning method and artificial rules method are the two major methods used.Discusses the integration of statistical learning method and artificial rule method for PP recognition based on several typical PP recognition model in the shallow parsing level,and then points out that the feature extraction is an abstract of the pragmatic rules based on corpus.Proposes that the combination of statistical learning methods and artificial rule methods is the future direction of development.

Creation of topic map by identifying topic chain in chinese

Abstract: XML Topic maps enable multiple concurrent views of sets of information objects and can be used to different applications. For example thesaurus-like interfaces to corpora navigational tools for cross-references or citation systems information filtering or delivering depending on user profiles etc. However to enrich the information of a topic map or to connect with some document's URI is very labor-intensive and time-consuming. To solve this problem we propose an approach based on natural language processing techniques to identify and extract useful information in raw Chinese text. Unlike most traditional approaches to parsing sentences based on the integration of complex linguistic information and domain knowledge we work on the output of a part-of-speech tagger and use shallow parsing instead of complex parsing to identify the topics of sentences. The key elements of the centering model of local discourse coherence are employed to extract structures of discourse segments. We use the local discourse structure to solve the problem of zero anaphora in Chinese and then identify the topic which is the most salient element in a sentence. After we obtain all the topics of a document we may assign this document into a topic node of the topic map and add the information of the document into the topic element simultaneously.