Influence of personal choices on lexical variability in referring expressions

2015 ◽  
Vol 22 (2) ◽  
pp. 257-290 ◽  
Author(s):  
RAQUEL HERVÁS ◽  
JAVIER ARROYO ◽  
VIRGINIA FRANCISCO ◽  
FEDERICO PEINADO ◽  
PABLO GERVÁS
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Methodological Approach ◽  
Human Language ◽  
Referring Expressions ◽  
New Information ◽  
Communicative Act

AbstractVariability is inherent in human language as different people make different choices when facing the same communicative act. In Natural Language Processing, variability is a challenge. It hinders some tasks such as evaluation of generated expressions, while it constitutes an interesting resource to achieve naturalness and to avoid repetitiveness. In this work, we present a methodological approach to study the influence of lexical variability. We apply this approach to TUNA, a corpus of referring expression lexicalizations, in order to study the use of different lexical choices. First, we reannotate the TUNA corpus with new information about lexicalization, and then we analyze this reannotation to study how people lexicalize referring expressions. The results show that people tend to be consistent when generating referring expressions. But at the same time, different people also share certain preferences.

scholarly journals A Natural Language Processing Approach to Automated Highlighting of New Information in Clinical Notes

2020 ◽  
Vol 10 (8) ◽  
pp. 2824
Author(s):  
Yu-Hsiang Su ◽  
Ching-Ping Chao ◽  
Ling-Chien Hung ◽  
Sheng-Feng Sung ◽  
Pei-Ju Lee
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Task Performance ◽  
Language Processing ◽  
Automated Identification ◽  
Clinical Notes ◽  
New Information ◽  
Perceived Workload ◽  
The Impact ◽  
User Experiment

Electronic medical records (EMRs) have been used extensively in most medical institutions for more than a decade in Taiwan. However, information overload associated with rapid accumulation of large amounts of clinical narratives has threatened the effective use of EMRs. This situation is further worsened by the use of “copying and pasting”, leading to lots of redundant information in clinical notes. This study aimed to apply natural language processing techniques to address this problem. New information in longitudinal clinical notes was identified based on a bigram language model. The accuracy of automated identification of new information was evaluated using expert annotations as the reference standard. A two-stage cross-over user experiment was conducted to evaluate the impact of highlighting of new information on task demands, task performance, and perceived workload. The automated method identified new information with an F1 score of 0.833. The user experiment found a significant decrease in perceived workload associated with a significantly higher task performance. In conclusion, automated identification of new information in clinical notes is feasible and practical. Highlighting of new information enables healthcare professionals to grasp key information from clinical notes with less perceived workload.

scholarly journals SEQ2SEQ VS SKETCH FILLING STRUCTURE FOR NATURAL LANGUAGE TO SQL TRANSLATION

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 7-11
Author(s):  
K. Ahkouk ◽  
M. Machkour ◽  
K. Majhadi ◽  
R. Mama
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Human Language ◽  
Test Results ◽  
Exact Matching ◽  
Cross Domain ◽  
Pros And Cons ◽  
Time Required

Abstract. Sequence to sequence models have been widely used in the recent years in the different tasks of Natural Language processing. In particular, the concept has been deeply adopted to treat the problem of translating human language questions to SQL. In this context, many studies suggest the use of sequence to sequence approaches for predicting the target SQL queries using the different available datasets. In this paper, we put the light on another way to resolve natural language processing tasks, especially the Natural Language to SQL one using the method of sketch-based decoding which is based on a sketch with holes that the model incrementally tries to fill. We present the pros and cons of each approach and how a sketch-based model can outperform the already existing solutions in order to predict the wanted SQL queries and to generate to unseen input pairs in different contexts and cross-domain datasets, and finally we discuss the test results of the already proposed models using the exact matching scores and the errors propagation and the time required for the training as metrics.

scholarly journals Text: An R-package for Analyzing and Visualizing Human Language Using Natural Language Processing and Deep Learning

2021 ◽  
Author(s):  
Oscar Nils Erik Kjell ◽  
H. Andrew Schwartz ◽  
Salvatore Giorgi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rating Scale ◽  
State Of The Art ◽  
R Package ◽  
Language Models ◽  
Categorical Variables ◽  
Human Language

The language that individuals use for expressing themselves contains rich psychological information. Recent significant advances in Natural Language Processing (NLP) and Deep Learning (DL), namely transformers, have resulted in large performance gains in tasks related to understanding natural language such as machine translation. However, these state-of-the-art methods have not yet been made easily accessible for psychology researchers, nor designed to be optimal for human-level analyses. This tutorial introduces text (www.r-text.org), a new R-package for analyzing and visualizing human language using transformers, the latest techniques from NLP and DL. Text is both a modular solution for accessing state-of-the-art language models and an end-to-end solution catered for human-level analyses. Hence, text provides user-friendly functions tailored to test hypotheses in social sciences for both relatively small and large datasets. This tutorial describes useful methods for analyzing text, providing functions with reliable defaults that can be used off-the-shelf as well as providing a framework for the advanced users to build on for novel techniques and analysis pipelines. The reader learns about six methods: 1) textEmbed: to transform text to traditional or modern transformer-based word embeddings (i.e., numeric representations of words); 2) textTrain: to examine the relationships between text and numeric/categorical variables; 3) textSimilarity and 4) textSimilarityTest: to computing semantic similarity scores between texts and significance test the difference in meaning between two sets of texts; and 5) textProjection and 6) textProjectionPlot: to examine and visualize text within the embedding space according to latent or specified construct dimensions (e.g., low to high rating scale scores).

scholarly journals An Analysis of the Applications of Natural Language Processing in Various Sectors

2021 ◽  
Author(s):  
Priya B ◽  
Nandhini J.M ◽  
Gnanasekaran T
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Health Sector ◽  
Automated System ◽  
Human Language ◽  
Agriculture Sector ◽  
Prediction Of Diabetes

Natural Language processing (NLP) dealing with Artificial Intelligence concept is a subfield of Computer Science, enabling computers to understand and process human language. Natural Language Processing being a part of artificial intelligence provides understanding of human language by computers for the purpose of extracting information or insights and create meaningful response. It involves creating algorithms that transform text in to words labeling With the emerging advancements in Machine learning and Deep Learning, NLP can contributed a lot towards health sector, education, agriculture and so on. This paper summarizes the various aspects of NLP along with case studies associated with Health Sector for Voice Automated System, prediction of Diabetes Millets, Crop Detection technique in Agriculture Sector.

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