scholarly journals A conceptual similarity and correlation discrimination method based on HowNet

2020 ◽  
Vol 309 ◽  
pp. 03020
Author(s):  
Yunnian Ding ◽  
Yangli Jia ◽  
Zhenling Zhang
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Theoretical Basis ◽  
Important Research ◽  
Part Of Speech ◽  
Wide Range ◽  
Calculation Results ◽  
Definition Of ◽  
Application Fields ◽  
Discrimination Method

The similarity and correlation analysis of word concepts has a wide range of applications in natural language processing, and has important research significance in information retrieval, text classification, data mining, and other application fields. This paper analyzes and summarizes the information of sememes relationship through the definition of words in HowNet and proposes a method to distinguish the similarity and correlation of words. Firstly, using a combination of the part of speech and sememes to distinguish the similarity and correlation between words concept. Secondly, the similarity and correlation calculation results between vocabulary concepts are used to further optimize the judgment results. Finally, the similarity and correlation distinction and discrimination between vocabulary concepts are realized. The experimental results show that the method reduces the complexity of the algorithm and greatly improves the work efficiency. The semantic similarity and correlation judgment results are more in line with the human intuitive experience and improve the accuracy of computer understanding of natural language. which provides an important theoretical basis for the development of natural language.

Using Uniform-Design GEP for Part-of-Speech Tagging

2016 ◽  
Vol 26 (04) ◽  
pp. 1750060 ◽  
Author(s):  
Chengyao Lv ◽  
Huihua Liu ◽  
Yuanxing Dong ◽  
Fangyuan Li ◽  
Yuan Liang
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Uniform Design ◽  
Accuracy Rate ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Proposed Model ◽  
Wide Range ◽  
Unknown Words ◽  
Pos Tagger

In natural language processing (NLP), a crucial subsystem in a wide range of applications is a part-of-speech (POS) tagger, which labels (or classifies) unannotated words of natural language with POS labels corresponding to categories such as noun, verb or adjective. This paper proposes a model of uniform-design genetic expression programming (UGEP) for POS tagging. UGEP is used to search for appropriate structures in function space of POS tagging problems. After the evolution of sequence of tags, GEP can find the best individual as solution. Experiments on Brown Corpus show that (1) in closed lexicon tests, UGEP model can get higher accuracy rate of 98.8% which is much better than genetic algorithm model, neural networks and hidden Markov model (HMM) model.; (2) in open lexicon tests, the proposed model can also achieve higher accuracy rate of 97.4% and a high accuracy rate on unknown words of 88.6%.

Assessment of Certainty of Ventilation Processes Mathematical Modeling

2015 ◽  
Vol 725-726 ◽  
pp. 1255-1260
Author(s):  
Tamara Daciuk ◽  
Vera Ulyasheva
Keyword(s):  
Mathematical Modeling ◽  
Turbulent Flows ◽  
Turbulence Models ◽  
Field Measurements ◽  
Heat Emission ◽  
Emission Sources ◽  
Wide Range ◽  
Calculation Results ◽  
Definition Of ◽  
Semiempirical Models

Numerical experiment has been successfully used during recent 10-15 years to solve a wide range of thermal and hydrogasodynamic tasks. Application of mathematical modeling used to design the ventilation systems for production premises characterized by heat emission may be considered to be an effective method to obtain reasonable solutions. Results of calculation performed with numerical solution of ventilation tasks depend on turbulence model selection. Currently a large number of different turbulence models used to calculate turbulent flows are known. Testing and definition of applicability limits for semiempirical models of turbulence should be considered to be a preliminary stage of calculation. This article presents results of test calculations pertaining to thermal air process modeling in premises characterized by presence of heat emission sources performed with employment of different models of turbulence. Besides, analysis of calculation results and comparison with field measurements data are presented.

scholarly journals Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Clifford Nangle ◽  
Stuart McTaggart ◽  
Margaret MacLeod ◽  
Jackie Caldwell ◽  
Marion Bennie
Keyword(s):  
Information System ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Drug Exposure ◽  
Drug Dose ◽  
Free Text ◽  
Wide Range ◽  
The Impact ◽  
Prescribing Information

ABSTRACT ObjectivesThe Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume. The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness. ApproachAn NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage. ResultsThe NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively. ConclusionThe NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure.

A Framework for the Generation of Class Diagram from Text Requirements using Natural language Processing

2021 ◽  
Vol 10 (1) ◽  
pp. 25-31
Keyword(s):  
Natural Language Processing ◽  
Software Development ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Software Requirements ◽  
Class Diagram ◽  
Requirement Analysis ◽  
Part Of Speech ◽  
Software Engineers

The software development procedure begins with identifying the requirement analysis. The process levels of the requirements start from analysing the requirements to sketch the design of the program, which is very critical work for programmers and software engineers. Moreover, many errors will happen during the requirement analysis cycle transferring to other stages, which leads to the high cost of the process more than the initial specified process. The reason behind this is because of the specifications of software requirements created in the natural language. To minimize these errors, we can transfer the software requirements to the computerized form by the UML diagram. To overcome this, a device has been designed, which plans can provide semi-automatized aid for designers to provide UML class version from software program specifications using natural Language Processing techniques. The proposed technique outlines the class diagram in a well-known configuration and additionally facts out the relationship between instructions. In this research, we propose to enhance the procedure of producing the UML diagrams by utilizing the Natural Language, which will help the software development to analyze the software requirements with fewer errors and efficient way. The proposed approach will use the parser analyze and Part of Speech (POS) tagger to analyze the user requirements entered by the user in the English language. Then, extract the verbs and phrases, etc. in the user text. The obtained results showed that the proposed method got better results in comparison with other methods published in the literature. The proposed method gave a better analysis of the given requirements and better diagrams presentation, which can help the software engineers. Key words: Part of Speech,UM

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

Machine Learning in Natural Language Processing

2010 ◽  
pp. 302-324
Author(s):  
Marina Sokolova ◽  
Stan Szpakowicz
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Word Sense Disambiguation ◽  
Machine Learning Techniques ◽  
Word Sense ◽  
Part Of Speech ◽  
Applications Of Machine Learning

This chapter presents applications of machine learning techniques to traditional problems in natural language processing, including part-of-speech tagging, entity recognition and word-sense disambiguation. People usually solve such problems without difficulty or at least do a very good job. Linguistics may suggest labour-intensive ways of manually constructing rule-based systems. It is, however, the easy availability of large collections of texts that has made machine learning a method of choice for processing volumes of data well above the human capacity. One of the main purposes of text processing is all manner of information extraction and knowledge extraction from such large text. Machine learning methods discussed in this chapter have stimulated wide-ranging research in natural language processing and helped build applications with serious deployment potential.

A Persian Medical Question Answering System

2020 ◽  
Vol 29 (06) ◽  
pp. 2050019
Author(s):  
Hadi Veisi ◽  
Hamed Fakour Shandi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Document Retrieval ◽  
Main Concept ◽  
Question Answering System ◽  
Detection Algorithms ◽  
Persian Language ◽  
Part Of Speech ◽  
Answer Extraction

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three main modules: question processing, document retrieval, and answer extraction. For the question processing module, a sequential architecture is designed which retrieves the main concept of a question by using different components. In these components, rule-based methods, natural language processing, and dictionary-based techniques are used. In the document retrieval module, the documents are indexed and searched using the Lucene library. The retrieved documents are ranked using similarity detection algorithms and the highest-ranked document is selected to be used by the answer extraction module. This module is responsible for extracting the most relevant section of the text in the retrieved document. During this research, different customized language processing tools such as part of speech tagger and lemmatizer are also developed for Persian. Evaluation results show that this system performs well for answering different questions about diseases and drugs. The accuracy of the system for 500 sample questions is 83.6%.

Constructing Corpora for the Development and Evaluation of Paraphrase Systems

2008 ◽  
Vol 34 (4) ◽  
pp. 597-614 ◽  
Author(s):  
Trevor Cohn ◽  
Chris Callison-Burch ◽  
Mirella Lapata
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syntactic Structure ◽  
Nominal Data ◽  
Parallel Corpus ◽  
Word Alignments ◽  
Definition Of

Automatic paraphrasing is an important component in many natural language processing tasks. In this article we present a new parallel corpus with paraphrase annotations. We adopt a definition of paraphrase based on word alignments and show that it yields high inter-annotator agreement. As Kappa is suited to nominal data, we employ an alternative agreement statistic which is appropriate for structured alignment tasks. We discuss how the corpus can be usefully employed in evaluating paraphrase systems automatically (e.g., by measuring precision, recall, and F1) and also in developing linguistically rich paraphrase models based on syntactic structure.

scholarly journals Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 218-239 ◽  
Author(s):  
Ravikumar Patel ◽  
Kalpdrum Passi
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
World Cup ◽  
Part Of Speech ◽  
Twitter Data ◽  
Processing Techniques

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

scholarly journals Reports of the Workshops Held at the Tenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 99-102
Author(s):  
Tiffany Barnes ◽  
Oliver Bown ◽  
Michael Buro ◽  
Michael Cook ◽  
Arne Eigenfeldt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
North Carolina ◽  
Natural Language Processing ◽  
Natural Language ◽  
Real Time ◽  
Language Processing ◽  
State University ◽  
North Carolina State University ◽  
Wide Range ◽  
Digital Entertainment

The AIIDE-14 Workshop program was held Friday and Saturday, October 3–4, 2014 at North Carolina State University in Raleigh, North Carolina. The workshop program included five workshops covering a wide range of topics. The titles of the workshops held Friday were Games and Natural Language Processing, and Artificial Intelligence in Adversarial Real-Time Games. The titles of the workshops held Saturday were Diversity in Games Research, Experimental Artificial Intelligence in Games, and Musical Metacreation. This article presents short summaries of those events.

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