Shallow parsing

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

Shallow semantic labeling using two-phase feature-enhanced string matching

Abstract: A two-phase annotation method for semantic labeling in natural language processing is proposed. The dynamic programming approach stresses on a non-exact string matching which takes full advantage of the underlying grammatical structure of the parse trees in a Treebank. The first phase of the labeling is a coarse-grained syntactic parsing which is complementary to a semantic dissimilarities analysis in its latter phase. The approach goes beyond shallow parsing to a deeper level of case role identification, while preserving robustness, without being bogged down into a complete linguistic analysis. The paper presents experimental results for recognizing more than 50 different semantic labels in 10,000 sentences. Results show that the approach improves the labeling, even though with incomplete information. Detailed evaluations are discussed in order to justify its significances.

SRCB-WSD: Supervised Chinese Word Sense Disambiguation with Key Features

Abstract: This article describes the implementation of Word Sense Disambiguation system that participated in the SemEval-2007 multilingual Chinese-English lexical sample task. We adopted a supervised learning approach with Maximum Entropy classifier. The features used were neighboring words and their part-of-speech, as well as single words in the context, and other syntactic features based on shallow parsing. In addition, we used word category information of a Chinese thesaurus as features for verb disambiguation. For the task we participated in, we obtained precision of 0.716 in micro-average, which is the best among all participated systems.

A Logic of Semantic Representations for Shallow Parsing

Abstract: One way to construct semantic representations in a robust manner is to enhance shallow language processors with semantic components. Here, we provide a model theory for a semantic formalism that is designed for this, namely Robust Minimal Recursion Semantics (rmrs). We show that rmrs supports a notion of entailment that allows it to form the basis for comparing the semantic output of different parses of varying depth.

Context-sensitive electronic dictionaries

Abstract: This paper introduces a context-sensitive electronic dictionary that provides translations for any piece of text displayed on a computer screen, without requiring user interaction. This is achieved through a process of three phases: text acquisition from the screen, morpho-syntactic analysis of the context of the selected word, and the dictionary lookup. As with other similar tools available, this program usually works with dictionaries adapted from one or more printed dictionaries. To implement context sensitive features, however, traditional dictionary entries need to be restructured. By splitting up entries into smaller pieces and indexing them in a special way, the program is able to display a restricted set of information that is relevant to the context. Based on the information in the dictionaries, the program is able to recognize---even discontinuous---multiword expressions on the screen.The program has three major features which we believe make it unique for the time being, and which the development focused on: linguistic flexibility (stemming, morphological analysis and shallow parsing), open architecture (three major architectural blocks, all replaceable along public documented APIs), and flexible user interface (replaceable dictionaries, direct user feedback).In this paper, we assess the functional requirements of a context-sensitive dictionary as a start; then we explain the program's three phases of operation, focusing on the implementation of the lexicons and the context-sensitive features. We conclude the paper by comparing our tool to other similar publicly available products, and summarize plans for future development.