Shallow parsing

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

Shallow parsing for Portuguese-Spanish machine translation

Abstract: Work funded by Portal Universia, S.A. and partially supported by the Spanish Comision Ministerial de Ciencia y Tecnologia through grant TIC2000-1599-CO2-02.

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.

An Information Extraction Model for Unconstrained Handwritten Documents

Abstract: In this paper, a new information extraction system by statistical shallow parsing in unconstrained handwritten documents is introduced Unlike classical approaches found in the literature as keyword spotting or full document recognition, our approch relies on a strong and powerful global handwriting model A entire text line is considered as an indivisible entity and is modeled with Hidden Markov Models In this way, text line shallow parsing allows fast extraction of the relevant information in any document while rejecting at the same time irrelevant information First results are promising and show the interest of the approach

Reranking Translation Hypotheses Using Structural Properties

Abstract: We investigate methods that add syntactically motivated features to a statistical machine translation system in a reranking framework The goal is to analyze whether shallow parsing techniques help in identifying ungrammatical hypotheses We show that improvements are possible by utilizing supertagging, lightweight dependency analysis, a link grammar parser and a maximum-entropy based chunk parser Adding features to n-best lists and discriminatively training the system on a development set increases the BLEU score up to 07% on the test set

Dynamic context generation for natural language understanding: a multifaceted knowledge approach

Abstract: We describe a comprehensive framework for text understanding, based on the representation of context. It is designed to serve as a representation of semantics for the full range of interpretive and inferential needs of general natural language processing. Its most distinctive feature is its uniform representation of the various simple and independent linguistic sources that play a role in determining meaning: lexical associations, syntactic restrictions, case-role expectations, and most importantly, contextual effects. Compositional syntactic structure from a shallow parsing is represented in a neural net-based associative memory, where it then interacts through a Bayesian network with semantic associations and the context or "gist" of the passage carried forward from preceding sentences. Experiments with more than 2000 sentences in different languages are included.