scholarly journals Efficient Estimate of Low-Frequency Words’ Embeddings Based on the Dictionary: A Case Study on Chinese

2021 ◽  
Vol 11 (22) ◽  
pp. 11018
Author(s):  
Xianwen Liao ◽  
Yongzhong Huang ◽  
Changfu Wei ◽  
Chenhao Zhang ◽  
Yongqing Deng ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Low Frequency ◽  
Word Embeddings ◽  
High Quality ◽  
Representation Model ◽  
Explanatory Note ◽  
Efficient Estimate

Obtaining high-quality embeddings of out-of-vocabularies (OOVs) and low-frequency words is a challenge in natural language processing (NLP). To efficiently estimate the embeddings of OOVs and low-frequency words, we propose a new method that uses the dictionary to estimate the embeddings of OOVs and low-frequency words. More specifically, the explanatory note of an entry in dictionaries accurately describes the semantics of the corresponding word. Naturally, we adopt the sentence representation model to extract the semantics of the explanatory note and regard the semantics as the embedding of the corresponding word. We design a new sentence representation model to encode sentences to extract the semantics from the explanatory notes of entries more efficiently. Based on the assumption that the higher quality of word embeddings will lead to better performance, we design an extrinsic experiment to evaluate the quality of low-frequency words’ embeddings. The experimental results show that the embeddings of low-frequency words estimated by our proposed method have higher quality. In addition, both intrinsic and extrinsic experiments show that our proposed sentence representation model can represent the semantics of sentences well.

scholarly journals Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili

2019 ◽  
Vol 9 (18) ◽  
pp. 3648
Author(s):  
Casper S. Shikali ◽  
Zhou Sijie ◽  
Liu Qihe ◽  
Refuoe Mokhosi
Keyword(s):  
Language Processing ◽  
Critical Role ◽  
Language Model ◽  
Central Africa ◽  
Spoken Language ◽  
Language Models ◽  
Word Embeddings ◽  
Word Representation

Deep learning has extensively been used in natural language processing with sub-word representation vectors playing a critical role. However, this cannot be said of Swahili, which is a low resource and widely spoken language in East and Central Africa. This study proposed novel word embeddings from syllable embeddings (WEFSE) for Swahili to address the concern of word representation for agglutinative and syllabic-based languages. Inspired by the learning methodology of Swahili in beginner classes, we encoded respective syllables instead of characters, character n-grams or morphemes of words and generated quality word embeddings using a convolutional neural network. The quality of WEFSE was demonstrated by the state-of-art results in the syllable-aware language model on both the small dataset (31.229 perplexity value) and the medium dataset (45.859 perplexity value), outperforming character-aware language models. We further evaluated the word embeddings using word analogy task. To the best of our knowledge, syllabic alphabets have not been used to compose the word representation vectors. Therefore, the main contributions of the study are a syllabic alphabet, WEFSE, a syllabic-aware language model and a word analogy dataset for Swahili.

scholarly journals Ambient virtual scribes: Mutuo Health’s AutoScribe as a case study of artificial intelligence-based technology

2019 ◽  
Vol 33 (1) ◽  
pp. 34-38 ◽  
Author(s):  
Noah H. Crampton
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Medical Record ◽  
Language Processing ◽  
Poor Quality ◽  
Health Data ◽  
Free Text ◽  
High Quality ◽  
Data Analytic

Studies show that clinicians are increasingly burning out in large part from the clerical burden associated with using Electronic Medical Record (EMR) systems. At the same time, recently developed health data analytic algorithms struggle with poor quality free-text entered data in these systems. We developed AutoScribe using artificial intelligence–based natural language processing tools to automate these clerical tasks and to output high-quality EMR data. In this article, we describe the benefits and drawbacks of our technology. Furthermore, we describe how we are positioning our company’s culture within the existing healthcare system and suggest steps leaders of the system should consider in order to ensure that potentially transformative artificial intelligence–based technologies like ours are optimally adopted.

scholarly journals The Value of Paraphrase for Knowledge Base Predicates

2020 ◽  
Vol 34 (05) ◽  
pp. 9346-9353
Author(s):  
Bingcong Xue ◽  
Sen Hu ◽  
Lei Zou ◽  
Jiashu Cheng
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Knowledge Bases ◽  
Human Beings ◽  
High Quality ◽  
Language Generation

Paraphrase, i.e., differing textual realizations of the same meaning, has proven useful for many natural language processing (NLP) applications. Collecting paraphrase for predicates in knowledge bases (KBs) is the key to comprehend the RDF triples in KBs. Existing works have published some paraphrase datasets automatically extracted from large corpora, but have too many redundant pairs or don't cover enough predicates, which cannot be improved by computer only and need the help of human beings. This paper shows a full process of collecting large-scale and high-quality paraphrase dictionaries for predicates in knowledge bases, which takes advantage of existing datasets and combines the technologies of machine mining and crowdsourcing. Our dataset comprises 2284 distinct predicates in DBpedia and 31130 paraphrase pairs in total, the quality of which is a great leap over previous works. Then it is demonstrated that such good paraphrase dictionaries can do great help to natural language processing tasks such as question answering and language generation. We also publish our own dictionary for further research.

scholarly journals Natural language inference by deep learning method

2022 ◽  
Vol 355 ◽  
pp. 03028
Author(s):  
Saihan Li ◽  
Zhijie Hu ◽  
Rong Cao
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Method ◽  
Word Embeddings ◽  
Learning Models ◽  
The Relationship ◽  
Inference Task

Natural Language inference refers to the problem of determining the relationships between a premise and a hypothesis, it is an emerging area of natural language processing. The paper uses deep learning methods to complete natural language inference task. The dataset includes 3GPP dataset and SNLI dataset. Gensim library is used to get the word embeddings, there are 2 methods which are word2vec and doc2vec to map the sentence to array. 2 deep learning models DNNClassifier and Attention are implemented separately to classify the relationship between the proposals from the telecommunication area dataset. The highest accuracy of the experiment is 88% and we found that the quality of the dataset decided the upper bound of the accuracy.

scholarly journals Using the Google™ Search Engine for Health Information: Is There a Problem? Case Study: Supplements for Cancer

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Hannah C Cai ◽  
Leanne E King ◽  
Johanna T Dwyer
Keyword(s):  
Health Information ◽  
Search Engine ◽  
Information Quality ◽  
Nutrition Information ◽  
High Quality ◽  
Search Results ◽  
Health And Nutrition ◽  
Quality Rating

ABSTRACT We assessed the quality of online health and nutrition information using a Google™ search on “supplements for cancer”. Search results were scored using the Health Information Quality Index (HIQI), a quality-rating tool consisting of 12 objective criteria related to website domain, lack of commercial aspects, and authoritative nature of the health and nutrition information provided. Possible scores ranged from 0 (lowest) to 12 (“perfect” or highest quality). After eliminating irrelevant results, the remaining 160 search results had median and mean scores of 8. One-quarter of the results were of high quality (score of 10–12). There was no correlation between high-quality scores and early appearance in the sequence of search results, where results are presumably more visible. Also, 496 advertisements, over twice the number of search results, appeared. We conclude that the Google™ search engine may have shortcomings when used to obtain information on dietary supplements and cancer.

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

scholarly journals Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Jacqueline Peng ◽  
Mengge Zhao ◽  
James Havrilla ◽  
Cong Liu ◽  
Chunhua Weng ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Manifestations ◽  
Predictive Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Research Articles

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

An open-source R-package and web application for high-quality probabilistic predictions in hydrology

2021 ◽  
Author(s):  
Jason Hunter ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
David McInerney
Keyword(s):  
Open Source ◽  
Web Application ◽  
R Package ◽  
Error Model ◽  
Objective Functions ◽  
High Quality ◽  
Wide Range ◽  
Probabilistic Error

<p>Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.</p><p>We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application.  </p><p>We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.</p><p>The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.</p>

scholarly journals Natural Language Processing (NLP) and Its Impact across Industries – Unlocking the True Potential of Digital Healthcare (A Case Study Approach)

2021 ◽  
pp. 86-98
Author(s):  
Sourajit Roy ◽  
Pankaj Pathak ◽  
S. Nithya
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Models ◽  
M2m Communication ◽  
Digital Revolution ◽  
Language Complexity ◽  
Study Approach ◽  
Data Variability

During the advent of the 21st century, technical breakthroughs and developments took place. Natural Language Processing or NLP is one of their promising disciplines that has been increasingly dynamic via groundbreaking findings on most computer networks. Because of the digital revolution the amounts of data generated by M2M communication across devices and platforms such as Amazon Alexa, Apple Siri, Microsoft Cortana, etc. were significantly increased. This causes a great deal of unstructured data to be processed that does not fit in with standard computational models. In addition, the increasing problems of language complexity, data variability and voice ambiguity make implementing models increasingly harder. The current study provides an overview of the potential and breadth of the NLP market and its acceptance in industry-wide, in particular after Covid-19. It also gives a macroscopic picture of progress in natural language processing research, development and implementation.

scholarly journals Sentiment of App with Word Vectors

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2156-2159
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Data ◽  
Vector Representations ◽  
Text Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.

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