Development Of Parser Research Articles

The Effectiveness of Bottom Up Technique with Probabilistic Approach for A Malay Parser

Parsing is a process of analyzing the input string in a sentence to define the syntax structures according to rules of grammar. This task is performed by a parser which will produce a parse tree as output. However, a problem occurs when the parsing process produces two or more parse trees in which the parser unable to represent a precise parse tree. This limitation is caused by ambiguity in the structure of sentences. Ambiguity is occurred when a word is classified more than one category of syntax and its usage will affect the semantics of the sentence. Thus, the parser needs to have an approach to solve the ambiguity problem and is able to process the most appropriate parse tree to present a sentence. Like other languages in the world, Malay language, a national language for Malaysian, is not exempted from ambiguity problem. However, due to its grammar being context-free grammar, the probabilistic context-free grammar approach can be used to support the parser in determining a more accurate parse tree. This study focuses on the development of statistical parser using a bottom-up technique for Malay language. The training data, in the form of simple Malay language sentences, are collected from various sources. Based on this training data, a statistical lexical corpus of Malay language which consists of vocabulary, grammar rules and their probability was developed. The bottom up parsing will be supported by implementing Cocke–Younger–Kasami (CYK) algorithm. The parser’s performance is evaluated based on its effectiveness to overcome ambiguity by suggesting a more precise parse tree. In conclusion, the Malay Language Parser can be useful to help user identify the appropriate parse tree and solve ambiguity issues in Malay Language.

Development of SAX parser working curriculum for the formation of semester workplans

Development of SAX parser working curriculum for the formation of semester workplans

Development and evaluation of a triple parser to enable visual searching with a biomedical search engine

We describe a new biomedical search engine that enables visual searching and utilises predicates instead of phrases. We report on the development and evaluation of the triple parser, the most essential component of the search engine, which extracts the necessary predicates from the biomedical text. Using texts from three biomedical related sites (N = 180), we compared the parser’s output with a gold standard independently created by a medical expert. The parser achieved more than 91% precision and recall. Its individual components showed different strengths with Finite State Automata being excellent for achieving high recall, while Support Vector Machines improved the precision.

Information Extraction Using Concept Node Analysis of Brain Radiology Reports Summarization

OBJECTIVE: Electronic Medical Record contains the majority of clinical data in unstructured text. The information in the textual document can be stored in conceptual format and used to support clinical care by text summarization technique. In this study, we present Information Extraction(IE) using Concept Node(CN) which is extraction rule in case frame from brain radiology reports in SNUH(Seoul National University Hospital) for summarization. METHOD: Following steps are performed: design conceptual model to define semantic entities as extraction templates of brain radiology report, build CN dictionary based on statistical syntactic pattern and development of parser to extract relevant information based on defined templates. RESULTS: The three evaluation results shows that 19% precision improvement after post processing supplemental specified complex verb construction and 19.24~21.25% accurate semantic effectiveness with extracting additional Korean noun. The average of precision is 85.18%, average of recall is 93.71% and F-measure is 0.89. CONCLUSION: Our approach has advantageous elements for different language at the same sentence. We expect this IE technology can summarize vast amount radiology texts material for clinical decision support system effectively and hope this study helps the evolution of clinical data representation in Korean medical records and its integration into the EMR in the future.