Shallow parsing

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

OPFI: A Tool for Opinion Finding in Polish.

Abstract: The paper contains a description of OPFI: Opinion Finder for the Polish Language, a freely available tool for opinion target extraction. The goal of the tool is opinion finding: a task of identifying tuples composed of sentiment (positive or negative) and its target (about what or whom is the sentiment expressed). OPFI is not dependent on any particular method of sentiment identification and provides a built-in sentiment dictionary as a convenient option. Technically, it contains implementations of three different modes of opinion tuple generation: one hybrid based on dependency parsing and CRF, the second based on shallow parsing and the third on deep learning, namely GRU neural network. The paper also contains a description of related language resources: two annotated treebanks and one set of tweets.

Study on Chinese named entity recognition based on cascaded conditional random fields

Abstract: Named entity recognition is one of the fundamental problems in many natural language processing applications,such as information extraction,information retrieval,machine translation,shallow parsing and question answering systemThis paper mainly researches the recognition of the complex location and complex organization in Chinese named entityThis paper presents a new algorithm of named entity recognition based on cascaded conditional random fieldsWe experimentally evaluate the algorithm on large-scale corpusIn open test,the recall,precision and F-measure achieves of 2 recognitions are 9195%,8999% ,9050% and 9007%,8872%,8939%

Shallow Parsing of INEX Queries

Abstract: This article presents the contribution of the LIPN : Laboratoire d’Informatique de Paris Nord (France) to the NLQ2NEXI (Natural Language Queries to NEXI) task (part of the Natural Language Processing (NLP) track) of the Initiative for Evaluation of XML Retrieval (INEX 2006) It discusses the use of shallow parsing methods to analyse natural language queries

Optimization of Internet Search based on Noun Phrases and Clustering Techniques

Abstract: Information Retrieval plays a vital role in our daily activities and its most prominent role marked in search engines. Retrieval of the relevant natural language text document is of more challenge. Typically, search engines are low precision in response to a query, retrieving lots of useless web pages, and missing some other important ones. In this paper, we present linguistic phenomena of NLP using shallow parsing and Chunking to extract the Noun Phrases. These noun phrases are used as key phrases to rank the documents (typically a list of titles and snippets returned by a certain Web search engine). Organizing Web search results in to clusters facilitates user‟s quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. Here, we also proposed an approach for web search results clustering based on a phrase based clustering algorithm Known as Optimized Snippet Flat Clustering (OSFC). It is an alternative to a single ordered result of search engines. This approach presents a list of clusters to the user. Experimental results verify our method's feasibility and effectiveness.

New Phrase Chunking Algorithm for Myanmar Natural Language Processing

Abstract: Chunking is the subdivision of sentences into non recursive regular syntactical groups: verbal chunks, nominal chunks, adjective chunks, adverbial chunks and propositional chunks etc. The chunker can operate as a preprocessor for Natural Language Processing systems. This study aims to proposed new phrase chunking algorithm for Myanmar natural language processing. The developed new algorithm accepts Myanmar tagged sentence as input and generates chunks as output. Input Myanmar sentence is split into chunks by using chunk markers such as postpositions, particles and conjunction and define the type of chunks as noun chunk, verb chunk, adjective chunk, adverb chunk and conjunction chunk. The algorithm was evaluated with POS tagged Myanmar sentences based on three measures parameters. According to the results, good accuracy of Precision, Recall and F-measure were obtained with new developed algorithm.