On the Usage of Semantic Text-Similarity Metrics for Natural Language Processing in Russian

2020 ◽  
Author(s):  
Mikhail Koroteev
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Similarity Metrics ◽  
Text Similarity

scholarly journals VNLP: Visible natural language processing

2021 ◽  
pp. 147387162110388
Author(s):  
Mohammad Alharbi ◽  
Matthew Roach ◽  
Tom Cheesman ◽  
Robert S Laramee
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Black Box ◽  
User Preferences ◽  
Text Similarity ◽  
Input Text ◽  
Visually Based ◽  
Pipeline Design

In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) pipeline design is then applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert.

scholarly journals The Case for Retaining Natural Language Descriptions of Phenotypes in Plant Databases and a Web Application as Proof of Concept

2021 ◽  
Author(s):  
Ian R. Braun ◽  
Diane C. Bassham ◽  
Carolyn J. Lawrence-Dill
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Free Text ◽  
Similarity Metrics ◽  
Proof Of Concept ◽  
Link Type ◽  
Plant Genes ◽  
Phenotype Similarity

ABSTRACTMotivationFinding similarity across phenotypic descriptions is not straightforward, with previous successes in computation requiring significant expert data curation. Natural language processing of free text phenotype descriptions is often easier to apply than intensive curation. It is therefore critical to understand the extent to which these techniques can be used to organize and analyze biological datasets and enable biological discoveries.ResultsA wide variety of approaches from the natural language processing domain perform as well as similarity metrics over curated annotations for predicting shared phenotypes. These approaches also show promise both for helping curators organize and work through large datasets as well as for enabling researchers to explore relationships among available phenotype descriptions. Here we generate networks of phenotype similarity and share a web application for querying a dataset of associated plant genes using these text mining approaches. Example situations and species for which application of these techniques is most useful are discussed.AvailabilityThe dataset used in this work is available at https://git.io/JTutQ. The code for the analysis performed here is available at https://git.io/JTutN and https://git.io/JTuqv. The code for the web application discussed here is available at https://git.io/Jtv9J, and the application itself is available at https://quoats.dill-picl.org/.

An Improved Text Similarity Calculation Algorithm Based on VSM

2011 ◽  
Vol 225-226 ◽  
pp. 1105-1108
Author(s):  
Lian Li ◽  
Ai Hong Zhu ◽  
Tao Su
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Clustering ◽  
Experimental Results ◽  
Measured Parameter ◽  
Text Similarity ◽  
Calculation Algorithm ◽  
Key Technology ◽  
Similarity Calculation

Text similarity calculation is a key technology in the fields of text clustering, Web intelligent retrieval and natural language processing etc. Because the traditional text similarity calculation algorithm does not consider the affect of same feature words between texts, sometimes this algorithm may lead to inaccurate results. To solve this problem, this paper gives an improved text similarity calculation algorithm. Considering that the amount of same feature words reflects two texts’ similarity in some extent, the improved algorithm adds in the coverage measured parameter, which effectively reduces the interference of texts with lower similarity. The simulation and experimental results verify the improved algorithm’s correctness and effectiveness.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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