Shallow parsing

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

Memory-Based Shallow Parsing

Abstract: We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this paper show competitive results, the F-value for the Wall Street Journal (WSJ) treebank is: 93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and 79.0% for object detection.

Shallow parsing using specialized hmms

Abstract: We present a unified technique to solve different shallow parsing tasks as a tagging problem using a Hidden Markov Model-based approach (HMM). This technique consists of the incorporation of the relevant information for each task into the models. To do this, the training corpus is transformed to take into account this information. In this way, no change is necessary for either the training or tagging process, so it allows for the use of a standard HMM approach. Taking into account this information, we construct a Specialized HMM which gives more complete contextual models. We have tested our system on chunking and clause identification tasks using different specialization criteria. The results obtained are in line with the results reported for most of the relevant state-of-the-art approaches.

Lightweight validation of natural language requirements

Abstract: In this paper, we report on our experiences of using lightweight formal methods for the partial validation of natural language requirements documents. We describe our approach to checking properties of models obtained by shallow parsing of natural language requirements, and apply it to a case study based on part of a NASA specification of the Node Control Software on the International Space Station. The experience reported supports our position that it is feasible and useful to perform automated analysis of requirements expressed in natural language. Indeed, we identified a number of errors in our case study that were also independently discovered and corrected by NASA's Independent Validation and Verification Facility in a subsequent version of the same document, and others that were not discovered. The paper describes the techniques we used, the errors we found and reflects on the lessons earned.

A Learning Approach to Shallow Parsing

Abstract: A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are presented. In doing that, we compare two ways of modeling the problem of learning to recognize patterns and suggest that shallow parsing patterns are better learned using open/close predictors than using inside/outside predictors.

A Learning Approach to Shallow Parsing

Abstract: A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are presented. In doing that, we compare two ways of modeling the problem of learning to recognize patterns and suggest that shallow parsing patterns are better learned using open/close predictors than using inside/outside predictors.} thus contribute to the understanding of how to model shallow parsing tasks as learning problems.