Topic
Shallow parsing
About: Shallow parsing is a research topic. Over the lifetime, 397 publications have been published within this topic receiving 10211 citations.
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IBM1
TL;DR: This article proposed a neural sequence chunking model that treats each chunk as a complete unit for labeling and achieved state-of-the-art performance on both text chunking and slot filling tasks.
Abstract: Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside-Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot filling tasks.
57 citations
TL;DR: The objective of this work is to develop an NLP infrastructure for Urdu that is customizable and capable of providing basic analysis on which more advanced information extraction tools can be built.
Abstract: There has been an increase in the amount of multilingual text on the Internet due to the proliferation of news sources and blogs. The Urdu language, in particular, has experienced explosive growth on the Web. Text mining for information discovery, which includes tasks such as identifying topics, relationships and events, and sentiment analysis, requires sophisticated natural language processing (NLP). NLP systems begin with modules such as word segmentation, part-of-speech tagging, and morphological analysis and progress to modules such as shallow parsing and named entity tagging. While there have been considerable advances in developing such comprehensive NLP systems for English, the work for Urdu is still in its infancy. The tasks of interest in Urdu NLP includes analyzing data sources such as blogs and comments to news articles to provide insight into social and human behavior. All of this requires a robust NLP system. The objective of this work is to develop an NLP infrastructure for Urdu that is customizable and capable of providing basic analysis on which more advanced information extraction tools can be built. This system assimilates resources from various online sources to facilitate improved named entity tagging and Urdu-to-English transliteration. The annotated data required to train the learning models used here is acquired by standardizing the currently limited resources available for Urdu. Techniques such as bootstrap learning and resource sharing from a syntactically similar language, Hindi, are explored to augment the available annotated Urdu data. Each of the new Urdu text processing modules has been integrated into a general text-mining platform. The evaluations performed demonstrate that the accuracies have either met or exceeded the state of the art.
55 citations
Journal Article•
TL;DR: The ProBot is interesting in its link to an underlying engine capable of implementing deeper reasoning, which is usually not present in conversational agents based on shallow parsing.
Abstract: This paper describes a conversational agent, called “ProBot”, that uses a novel structure for handling context. The ProBot is implemented as a rule-based system embedded in a Prolog interpreter. The rules consist of patterns and responses, where each pattern matches a user’s input sentence and the response is an output sentence. Both patterns and responses may have attached Prolog expressions that act as constraints in the patterns and can invoke some action when used in the response. The main contributions of this work are in the use of hierarchies of contexts to handle unexpected inputs. The ProBot is also interesting in its link to an underlying engine capable of implementing deeper reasoning, which is usually not present in conversational agents based on shallow parsing.
54 citations
20 Jun 1999
TL;DR: It is argued that the approach used in Humor 99 is general enough to be well suitable for a wide range of languages, and can serve as basis for higher-level linguistic operations such as shallow parsing.
Abstract: This paper introduces a new approach to morpho-syntactic analysis through Humor 99 ( H igh-speed U nification Mor phology), a reversible and unification-based morphological analyzer which has already been integrated with a variety of industrial applications. Humor 99 successfully copes with problems of agglutinative (e.g. Hungarian, Turkish, Estonian) and other (highly) inflectional languages (e.g. Polish, Czech, German) very effectively. The authors conclude the paper by arguing that the approach used in Humor 99 is general enough to be well suitable for a wide range of languages, and can serve as basis for higher-level linguistic operations such as shallow parsing.
53 citations
Journal Article•
TL;DR: A simple, developmentally motivated computational model that learns to comprehend and produce language when exposed to child-directed speech and uses backward transitional probabilities to create an inventory of ‘chunks’ consisting of one or more words.
53 citations