Topic
Deep linguistic processing
About: Deep linguistic processing is a research topic. Over the lifetime, 1497 publications have been published within this topic receiving 58459 citations.
Papers published on a yearly basis
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Papers
Book•
12 Jun 2009TL;DR: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
Abstract: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
4,331 citations
TL;DR: This book is already in probability information theory and linguistic found it should be well grounded and indeed it is, this foundational text in human language applications who want to create the way.
3,063 citations
TL;DR: This paper develops a computational technique for computing with words without any loss of information in the 2-tuple linguistic model and extends different classical aggregation operators to deal with this model.
Abstract: The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation of this approach imposed by its information representation model and the computation methods used when fusion processes are performed on linguistic values. This limitation is the loss of information; this loss of information implies a lack of precision in the final results from the fusion of linguistic information. In this paper, we present tools for overcoming this limitation. The linguistic information is expressed by means of 2-tuples, which are composed of a linguistic term and a numeric value assessed in (-0.5, 0.5). This model allows a continuous representation of the linguistic information on its domain, therefore, it can represent any counting of information obtained in a aggregation process. We then develop a computational technique for computing with words without any loss of information. Finally, different classical aggregation operators are extended to deal with the 2-tuple linguistic model.
2,755 citations
TL;DR: The amplitude of the N400 component of the e.r.p.ps was found to be an inverse function of the subject's expectancy for the terminal word as measured by its ‘Cloze probability’, which suggests N400 may reflect processes of semantic priming or activation.
Abstract: The neuroelectric activity of the human brain that accompanies linguistic processing can be studied through recordings of event-related potentials (erp components) from the scalp The erps triggered by verbal stimuli have been related to several different aspects of language processing For example, the N400 component, peaking around 400 ms post-stimulus, appears to be a sensitive indicator of the semantic relationship between a word and the context in which it occurs Words that complete sentences in a nonsensical fashion elicit much larger N400 waves than do semantically appropriate words or non-semantic irregularities in a text In the present study, erps were recorded in response to words that completed meaningful sentences The amplitude of the N400 component of the erp was found to be an inverse function of the subject's expectancy for the terminal word as measured by its 'Cloze probability' In addition, unexpected words that were semantically related to highly expected words elicited lower N400 amplitudes These findings suggest N400 may reflect processes of semantic priming or activation
2,203 citations
Journal Article•
TL;DR: Injection molding wherein a pair of separable mold plates are initially urged together and fluid plastic is injected into a mold cavity formed between the mold plates to form an article.
Abstract: Recently, there has been a rebirth of empiricism in the field of natural language processing. Manual encoding of linguistic information is being challenged by automated corpus-based learning as a method of providing a natural language processing system with linguistic knowledge. Although corpus-based approaches have been successful in many different areas of natural language processing, it is often the case that these methods capture the linguistic information they are modelling indirectly in large opaque tables of statistics. This can make it difficult to analyze, understand and improve the ability of these approaches to model underlying linguistic behavior. In this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learning method applied to part-of-speech tagging.
1,749 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 1 |
| 2024 | 8 |
| 2023 | 10 |
| 2022 | 23 |
| 2021 | 38 |
| 2020 | 29 |