scholarly journals Cross-linguistic automated detection of metaphors for poverty and cancer

2018 ◽  
Vol 10 (3) ◽  
pp. 467-493 ◽  
Author(s):  
OANA DAVID ◽  
TEENIE MATLOCK
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Social Problems ◽  
Conceptual Metaphor ◽  
Automated Detection ◽  
Analytic Tool ◽  
Corpus Linguistic ◽  
Recent Developments ◽  
Corpus Data

abstractConceptual metaphor research has benefited from advances in discourse analytic and corpus linguistic methodologies over the years, especially given recent developments with Natural Language Processing (NLP) technologies. Such technologies are now capable of identifying metaphoric expressions across large bodies of text. Here we focus on how one particular analytic tool, MetaNet, can be used to study everyday discourse about personal and social problems, in particular, poverty and cancer, by leveraging reusable networks of primary metaphors enhanced with specific metaphor subcases. We discuss the advantages of this approach in allowing us to gain valuable insights into cross-linguistic metaphor commonalities and variation. To demonstrate its utility, we analyze corpus data from English and Spanish.

scholarly journals Machine learning and natural language processing on the patent corpus: Data, tools, and new measures

2018 ◽  
Vol 27 (3) ◽  
pp. 535-553 ◽  
Author(s):  
Benjamin Balsmeier ◽  
Mohamad Assaf ◽  
Tyler Chesebro ◽  
Gabe Fierro ◽  
Kevin Johnson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Corpus Data

Recent Developments in Natural Language Processing

2021 ◽  
Author(s):  
Constantin Orasan ◽  
Ruslan Mitkov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Positive Impact ◽  
User Generated Content ◽  
Conversational Agents ◽  
Automatic Assessment ◽  
Reading Material ◽  
Multimodal Information ◽  
Recent Developments

Natural Language Processing (NLP) is a dynamic and rapidly developing field in which new trends, techniques, and applications are constantly emerging. This chapter focuses mainly on recent developments in NLP which could not be covered in other chapters of the Handbook. Topics such as crowdsourcing and processing of large datasets, which are no longer that recent but are widely used and not covered at length in any other chapter, are also presented. The chapter starts by describing how the availability of tools and resources has had a positive impact on the field. The proliferation of user-generated content has led to the emergence of research topics such as sarcasm and irony detection, automatic assessment of user-generated content, and stance detection. All of these topics are discussed in the chapter. The field of NLP is approaching maturity, a fact corroborated by the latest developments in the processing of texts for financial purposes and for helping users with disabilities, two topics that are also discussed here. The chapter presents examples of how researchers have successfully combined research in computer vision and natural language processing to enable the processing of multimodal information, as well as how the latest advances in deep learning have revitalized research on chatbots and conversational agents. The chapter concludes with a comprehensive list of further reading material and additional resources.

Natural language processing for automated detection of incidental durotomy

2020 ◽  
Vol 20 (5) ◽  
pp. 695-700 ◽  
Author(s):  
Aditya V. Karhade ◽  
Michiel E.R. Bongers ◽  
Olivier Q. Groot ◽  
Erick R. Kazarian ◽  
Thomas D. Cha ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automated Detection ◽  
Incidental Durotomy

scholarly journals Natural Language Processing and Its Challenges on Omotic Language Group of Ethiopia

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Girma Yohannis Bade
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Word Identification ◽  
Resource Limitation ◽  
Identification Problem ◽  
Digital Data ◽  
Data Resource ◽  
Language Groups ◽  
Recent Developments

This article reviews Natural Language Processing (NLP) and its challenge on Omotic language groups. All technological achievements are partially fuelled by the recent developments in NLP. NPL is one of component of an artificial intelligence (AI) and offers the facility to the companies that need to analyze their reliable business data. However, there are many challenges that tackle the effectiveness of NLP applications on Omotic language groups (Ometo) of Ethiopia. These challenges are irregularity of the words, stop word identification problem, compounding and languages ‘digital data resource limitation. Thus, this study opens the room to the upcoming researchers to further investigate the NLP application on these language groups.

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.

scholarly journals Bibliometric and Geographical Analysis of Cell Death Related Literature

2015 ◽  
Author(s):  
Vijaykumar Yogesh Muley ◽  
Anne Hahn ◽  
Pravin Paikrao
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Scientific Community ◽  
Official Language ◽  
Lexical Diversity ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Recent Developments ◽  
Speech Tagging

Natural language processing continues to gain importance in a thriving scientific community that communicates its latest results in such a frequency that following up on the most recent developments even in a specific field cannot be managed by human readers alone. Here we summarize and compare the publishing activity of the previous years on a distinct topic across several countries, addressing not only publishing frequency and history, but also stylistic characteristics that are accessible by means of natural language processing. Though there are no profound differences in the sentence lengths or lexical diversity among different countries, writing styles approached by Part-Of-Speech tagging are similar among countries that share history or official language or those are spatially close.

Lexical Knowledge Acquisition

2012 ◽  
Author(s):  
Yuji Matsumoto
Keyword(s):  
Natural Language Processing ◽  
Mutual Information ◽  
Natural Language ◽  
Language Processing ◽  
Lexical Knowledge ◽  
Lexical Representations ◽  
Domain Specific ◽  
Text Corpora ◽  
Corpus Data ◽  
Machine Readable

This article deals with the acquisition of lexical knowledge, instrumental in complementing the ambiguous process of NLP (natural language processing). Imprecise in nature, lexical representations are mostly simple and superficial. The thesaurus would be an apt example. Two primary tools for acquiring lexical knowledge are ‘corpora’ and ‘machine-readable dictionary’ (MRD). The former are mostly domain specific, monolingual, while the definitions in MRD are generally described by a ‘genus term’ followed by a set of differentiae. Auxiliary technical nuances of the acquisition process, find mention as well, such as ‘lexical collocation’ and ‘association’, referring to the deliberate co-occurrence of words that form a new meaning altogether and loses it whenever a synonym replaces either of the words. The first seminal work on collocation extraction from large text corpora, was compiled around the early 1990s, using inter-word mutual information to locate collocation. Abundant corpus data would be obtainable from the Linguistic Data Consortium (LDC).

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