Discourse Analysis and ANLP

2012 ◽  
pp. 33-52 ◽  
Author(s):  
Alexandra Kent ◽  
Philip M. McCarthy
Keyword(s):  
Natural Language Processing ◽  
Discourse Analysis ◽  
Language Processing ◽  
Future Directions ◽  
Everyday Language ◽  
Theoretical Assumptions ◽  
Central Meaning ◽  
The Many ◽  
Computational Resources ◽  
Tools And Techniques

The goal of this chapter is to outline a (primarily) qualitative and (secondarily) quantitative approach to the analysis of discourse. Discourse Analysis thrives on the variation and inconsistencies in our everyday language. Rather than focusing on what is said and seeking to reduce and homogenise accounts to find a central meaning, discourse analysis is interested in the consequences of “saying it that particular way at that particular time.” Put another way, it is interested in “what was said that didn’t have to be, and why?” and “what wasn’t said that could have been, and why not?” The chapter outlines the basic theoretical assumptions that underpin the many different methodological approaches within Discourse Analysis. It then considers these approaches in terms of the major themes of their research, the ongoing and future directions for study, and the scope of contribution to scientific knowledge that discourse analytic research can make. At the beginning and end of the chapter, we attempt to outline a role for Applied Natural Language Processing (ANLP) in Discourse Analysis. We discuss possible reasons for a lack of computational tools and techniques in traditional Discourse Analysis but we also offer suggestions as to the application of computational resources so that researchers in both disciplines might have an avenue of interest that assists their work, without directing it.

Using NLP for Fact Checking: A Survey

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
The Internet ◽  
Fake News ◽  
Fact Checking ◽  
The Many ◽  
Human Powered ◽  
The Web

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.

Natural Language Processing

1996 ◽  
Vol 16 ◽  
pp. 70-85 ◽  
Author(s):  
Thomas C. Rindflesch
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Domain Knowledge ◽  
The State ◽  
Point Of View ◽  
Computer Applications ◽  
Significant Progress ◽  
Future Directions

Work in computational linguistics began very soon after the development of the first computers (Booth, Brandwood and Cleave 1958), yet in the intervening four decades there has been a pervasive feeling that progress in computer understanding of natural language has not been commensurate with progress in other computer applications. Recently, a number of prominent researchers in natural language processing met to assess the state of the discipline and discuss future directions (Bates and Weischedel 1993). The consensus of this meeting was that increased attention to large amounts of lexical and domain knowledge was essential for significant progress, and current research efforts in the field reflect this point of view.

scholarly journals Towards a General-Purpose Linguistic Annotation Backend

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Graham Neubig ◽  
Patrick Littell ◽  
Chian-Yu Chen ◽  
Jean Lee ◽  
Zirui Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
General Purpose ◽  
Language Documentation ◽  
Training Material ◽  
Future Directions ◽  
Recent Advances ◽  
Linguistic Annotation

Language documentation is inherently a time-intensive process; transcription, glossing, and corpus management consume a significant portion of documentary linguists’ work. Advances in natural language processing can help to accelerate this work, using the linguists’ past decisions as training material, but questions remain about how to prioritize human involvement. In this extended abstract, we describe the beginnings of a new project that will attempt to ease this language documentation process through the use of natural language processing (NLP) technology. It is based on (1) methods to adapt NLP tools to new languages, based on recent advances in massively multilingual neural networks, and (2) backend APIs and interfaces that allow linguists to upload their data (§2). We then describe our current progress on two fronts: automatic phoneme transcription, and glossing (§3). Finally, we briefly describe our future directions (§4).

scholarly journals Authorship Weightage Algorithm for Academic Publications: A New Calculation and ACES Webserver for Determining Expertise

2021 ◽  
Vol 4 (2) ◽  
pp. 41
Author(s):  
Wei-Ling Wu ◽  
Owen Tan ◽  
Kwok-Fong Chan ◽  
Nicole Bernadette Ong ◽  
David Gunasegaran ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Group ◽  
Scoring System ◽  
Scoring Systems ◽  
The Public ◽  
Academic Fields ◽  
Academic Publications ◽  
The Many

Despite the public availability, finding experts in any field when relying on academic publications can be challenging, especially with the use of jargons. Even after overcoming these issues, the discernment of expertise by authorship positions is often also absent in the many publication-based search platforms. Given that it is common in many academic fields for the research group lead or lab head to take the position of the last author, some of the existing authorship scoring systems that assign a decreasing weightage from the first author would not reflect the last author correctly. To address these problems, we incorporated natural language processing (Common Crawl using fastText) to retrieve related keywords when using jargons as well as a modified authorship positional scoring that allows the assignment of greater weightage to the last author. The resulting output is a ranked scoring system of researchers upon every search that we implemented as a webserver for internal use called the APD lab Capability & Expertise Search (ACES).

scholarly journals Recent advances in processing negation

2020 ◽  
pp. 1-10
Author(s):  
Roser Morante ◽  
Eduardo Blanco
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Future Directions ◽  
Rule Based ◽  
Computational Approaches ◽  
Recent Advances ◽  
Linguistic Phenomenon

Abstract Negation is a complex linguistic phenomenon present in all human languages. It can be seen as an operator that transforms an expression into another expression whose meaning is in some way opposed to the original expression. In this article, we survey previous work on negation with an emphasis on computational approaches. We start defining negation and two important concepts: scope and focus of negation. Then, we survey work in natural language processing that considers negation primarily as a means to improve the results in some task. We also provide information about corpora containing negation annotations in English and other languages, which usually include a combination of annotations of negation cues, scopes, foci, and negated events. We continue the survey with a description of automated approaches to process negation, ranging from early rule-based systems to systems built with traditional machine learning and neural networks. Finally, we conclude with some reflections on current progress and future directions.

scholarly journals Analyzing Architectures for Neural Machine Translation using Low Computational Resources

2021 ◽  
Vol 10 (5) ◽  
pp. 9-16
Author(s):  
Aditya Mandke ◽  
Onkar Litake ◽  
Dipali Kadam
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Time Constraints ◽  
Neural Machine Translation ◽  
Recent Developments ◽  
Computationally Expensive ◽  
Computational Resources

With the recent developments in the field of Natural Language Processing, there has been a rise in the use of different architectures for Neural Machine Translation. Transformer architectures are used to achieve state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such setups consisting of high-end GPUs and other resources. We train our models on low computational resources and investigate the results. As expected, transformers outperformed other architectures, but there were some surprising results. Transformers consisting of more encoders and decoders took more time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively less time to train than transformers, making it suitable to use in situations having time constraints.

NATURAL LANGUAGE PROCESSING IN HEALTHCARE

2018 ◽  
Vol 8 (5) ◽  
pp. 39
Author(s):  
Matthew N.O. Sadiku ◽  
Yu Zhou ◽  
Sarhan M. Musa
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Human Mind ◽  
Cognitive Capacity ◽  
Written Communication ◽  
Computer Algorithms ◽  
Everyday Language

Natural language processing (NLP)  refers to the process of using of computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written communication.  Healthcare is the biggest user of the NLP tools. It is expected that NLP tools should  be able to bridge the gap between the mountain of data generated daily and the limited cognitive capacity of the human mind.  This paper provides a brief introduction on the use of NLP in healthcare.

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