Noun Phrase Identification Research Articles

Development of Arabic Noun Phrase Extractor (ANPE)

Extracting key phrases from documents is a common task in many applications. In general: The Noun Phrase Extractor consists of three modules: tokenization; part-of-speech tagging; noun phrase identification. These will be used as three main steps in building the new system ANPE, This paper aims at picking Arabic Noun Phrases from a corpus of documents, Relevant criteria (Recall and Precision), will be used as evaluation measure. On the one hand, when using NPs rather than using single terms, the system yields more relevant documents from the retrieved ones, on the other hand, it gave low precision because number of the retrieved documents will be decreased. At the researchers conclude and recommend improvements for more effective and efficient research in the future.

Towards Arabic Noun Phrase Extractor (ANPE) Using Information Retrieval Techniques

Information Retrieval system aims to help people find relevant information when they request it. This paper is interested in Noun Phrases (NPs) for Arabic language; generally the Noun Phrase Extractor is made up of three modules: tokenization; part-of-speech tagging; noun phrase identification. Also we will preview Noun Phrase Extractors, compare between them according to their suitability for building the new system, We focused on the effect of using the Arabic noun phrases that has been extracted on the information retrieval system in the light of computing measures of evaluation (Recall and Precision), we found that we get more relevant documents from the retrieved ones when using NPs rather than using single terms, on the other side number of retrieved documents will be decreased.

Improved Identification of Noun Phrases in Clinical Radiology Reports Using a High-Performance Statistical Natural Language Parser Augmented with the UMLS Specialist Lexicon

ObjectiveThe aim of this study was to develop and evaluate a method of extracting noun phrases with full phrase structures from a set of clinical radiology reports using natural language processing (NLP) and to investigate the effects of using the UMLS® Specialist Lexicon to improve noun phrase identification within clinical radiology documents. DesignThe noun phrase identification (NPI) module is composed of a sentence boundary detector, a statistical natural language parser trained on a nonmedical domain, and a noun phrase (NP) tagger. The NPI module processed a set of 100 XML-represented clinical radiology reports in Health Level 7 (HL7)® Clinical Document Architecture (CDA)–compatible format. Computed output was compared with manual markups made by four physicians and one author for maximal (longest) NP and those made by one author for base (simple) NP, respectively. An extended lexicon of biomedical terms was created from the UMLS Specialist Lexicon and used to improve NPI performance. ResultsThe test set was 50 randomly selected reports. The sentence boundary detector achieved 99.0% precision and 98.6% recall. The overall maximal NPI precision and recall were 78.9% and 81.5% before using the UMLS Specialist Lexicon and 82.1% and 84.6% after. The overall base NPI precision and recall were 88.2% and 86.8% before using the UMLS Specialist Lexicon and 93.1% and 92.6% after, reducing false-positives by 31.1% and false-negatives by 34.3%. ConclusionThe sentence boundary detector performs excellently. After the adaptation using the UMLS Specialist Lexicon, the statistical parser's NPI performance on radiology reports increased to levels comparable to the parser's native performance in its newswire training domain and to that reported by other researchers in the general nonmedical domain.

Memory-based shallow parsing

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement.