Shallow parsing

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

Making Shallow Look Deeper: Anaphora and Comparisons in Medical Information Extraction

Abstract: The paper focuses on resolving natural language issues which have been affecting performance of our system processing Polish medical data. In particular, we address phenomena such as ellipsis, anaphora, comparisons, coordination and negation occurring in mammogram reports. We propose practical data-driven solutions which allow us to improve the system's performance. and is based on Information Extraction (IE) techniques (shallow parsing and keyword recognition), which enables more efficient data processing than a deep text analysis. Although shallow methods are in principle sufficient for this task, natural language phenomena such as ellipsis, comparisons, coordination, anaphora or negation affect quality of the system's results. In this paper we present a few practical solutions, tailored for this application. The organization of the paper is as follows: first, we briefly present the system and its components; then, lin- guistic phenomena related to the analysis of three specific types of information recognized by the system (localiza- tion of mammographic findings, anatomical changes and comparison with previous examinations) are discussed, and the solutions adopted in the system are presented. The paper closes with a demonstration of the effect the im- provements have on the system's performance.

Automatic identifying of maximal length noun phrase

Abstract: The automatic recognition of the maximal-length noun phrase (MNP) helps to the shallow parsing. In this paper, automatic labeling of Chinese MNP is regarded as a sequential labeling task and Support Vector Machine model (SVM) is employed in the model. We propose a method which takes 2-phase hybrid approach which first identifies base chunk and then identifies MNP. Furthermore, the base chunk features can be exploited to improve performance of MNP recognition. In addition, both left-right and right-left sequential labeling were employed to identify Chinese MNP by bidirectional sequence labeling merging. The data set in the experiments is selected from Penn Chinese Treebank 5.0 Corpus, and split into train set, development set and test set according to the proportion of 4:4:1. Experimental result shows a high quality performance of 90.13% in F1-measure.

Automated Detection of Syntactic Ambiguity Using Shallow Parsing and Web Data

Abstract: Technical documents are mostly written in natural languages and they are highly ambiguity-prone due to the fact that ambiguity is an inevitable feature of natural languages. Many researchers have urged technical documents to be free from ambiguity to avoid unwanted and, in some cases, disastrous consequences ambiguity and misunderstanding can have in technical context. Therefore the need for ambiguity detection tools to assist writers with ambiguity detection and resolution seems indispensable. The purpose of this thesis work is to propose an automated approach in detection and resolution of syntactic ambiguity. AmbiGO is the name of the prototyping web application that has been developed for this thesis which is freely available on the web. The hope is that a developed version of AmbiGO will assist users with ambiguity detection and resolution. Currently AmbiGO is capable of detecting and resolving three types of syntactic ambiguity, namely analytical, coordination and PP attachment types. AmbiGO uses syntactic parsing to detect ambiguity patterns and retrieves frequency counts from Google for each possible reading as a segregate for semantic analysis. Such semantic analysis through Google frequency counts has significantly improved the precision score of the tool’s output in all three ambiguity detection functions. AmbiGO is available at this URL: http://omidemon.pythonanywhere.com/

Word Level Language Identification in English Telugu Code Mixed Data

Abstract: In a multilingual or sociolingual configuration Intra-sentential Code Switching (ICS) or Code Mixing (CM) is frequently observed nowadays. In the world, most of the people know more than one language. CM usage is especially apparent in social media platforms. Moreover, ICS is particularly significant in the context of technology, health, and law where conveying the upcoming developments are difficult in one's native language. In applications like dialog systems, machine translation, semantic parsing, shallow parsing, etc. CM and Code Switching pose serious challenges. To do any further advancement in code-mixed data, the necessary step is Language Identification. In this paper, we present a study of various models - Nave Bayes Classifier, Random Forest Classifier, Conditional Random Field (CRF), and Hidden Markov Model (HMM) for Language Identification in English - Telugu Code Mixed Data. Considering the paucity of resources in code mixed languages, we proposed the CRF model and HMM model for word level language identification. Our best performing system is CRF-based with an f1-score of 0.91.

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 re...