Making Sense of Unstructured Natural Language Information

2015 ◽  
pp. 173-183
Author(s):  
Kellyn Rein
Keyword(s):  
Natural Language ◽  
Making Sense

scholarly journals Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

2017 ◽  
Vol 26 (01) ◽  
pp. 228-234 ◽  
Author(s):  
A. Névéol ◽  
P. Zweigenbaum
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Bibliographic Databases ◽  
Coreference Resolution ◽  
Gradable Adjectives ◽  
Research Results ◽  
Making Sense ◽  
Textual Data ◽  
Clinical Natural Language Processing

Summary Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.

scholarly journals Natural Language Understanding: Instructions for (Present and Future) Use

2018 ◽  
Author(s):  
Roberto Navigli
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Language Understanding ◽  
Making Sense ◽  
Key Dimensions

In this paper I look at Natural Language Understanding, an area of Natural Language Processing aimed at making sense of text, through the lens of a visionary future: what do we expect a machine should be able to understand? and what are the key dimensions that require the attention of researchers to make this dream come true?

scholarly journals A Study on the journey of Natural Language Processing models: from Symbolic Natural Language Processing to Bidirectional Encoder Representations from Transformers

2021 ◽  
pp. 331-345
Author(s):  
Rajarshi SinhaRoy
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Computational Process ◽  
Historical Aspects ◽  
Making Sense ◽  
Digital Era ◽  
History Of

In this digital era, Natural language Processing is not just a computational process rather it is a way to communicate with machines as humanlike. It has been used in several fields from smart artificial assistants to health or emotion analyzers. Imagine a digital era without Natural language processing is something which we cannot even think of. In Natural language Processing, firstly it reads the information given and after that begins making sense of the information. After the data has been properly processed, the real steps are taken by the machine throwing some responses or completing the work. In this paper, I review the journey of natural language processing from the late 1940s to the present. This paper also contains several salient and most important works in this timeline which leads us to where we currently stand in this field. The review separates four eras in the history of Natural language Processing, each marked by a focus on machine translation, artificial intelligence impact, the adoption of a logico-grammatical style, and an attack on huge linguistic data. This paper helps to understand the historical aspects of Natural language processing and also inspires others to work and research in this domain.

scholarly journals Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

2017 ◽  
Vol 26 (01) ◽  
pp. 228-233
Author(s):  
A. Névéol ◽  
P. Zweigenbaum
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Bibliographic Databases ◽  
Coreference Resolution ◽  
Gradable Adjectives ◽  
Research Results ◽  
Making Sense ◽  
Textual Data ◽  
Clinical Natural Language Processing

Summary Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.

scholarly journals How quantifying the shape of stories predicts their success

2021 ◽  
Vol 118 (26) ◽  
pp. e2011695118
Author(s):  
Olivier Toubia ◽  
Jonah Berger ◽  
Jehoshua Eliashberg
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Space ◽  
Machine Learning Techniques ◽  
Making Sense ◽  
Cross Domain ◽  
Learning Techniques ◽  
Tv Shows ◽  
Number Of Citations

Narratives, and other forms of discourse, are powerful vehicles for informing, entertaining, and making sense of the world. But while everyday language often describes discourse as moving quickly or slowly, covering a lot of ground, or going in circles, little work has actually quantified such movements or examined whether they are beneficial. To fill this gap, we use several state-of-the-art natural language-processing and machine-learning techniques to represent texts as sequences of points in a latent, high-dimensional semantic space. We construct a simple set of measures to quantify features of this semantic path, apply them to thousands of texts from a variety of domains (i.e., movies, TV shows, and academic papers), and examine whether and how they are linked to success (e.g., the number of citations a paper receives). Our results highlight some important cross-domain differences and provide a general framework that can be applied to study many types of discourse. The findings shed light on why things become popular and how natural language processing can provide insight into cultural success.

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