A Framework for Feature Selection using Natural Language Processing for User Profile Learning for Recommendations of Healthcare Related Content

2022 ◽  
Vol 9 (3) ◽  
pp. 0-0
Keyword(s):  
Language Processing ◽  
Semantic Analysis ◽  
State Of The Art ◽  
User Profile ◽  
User Profiles ◽  
User Interest ◽  
Multiple User ◽  
Work Done ◽  
Relevant Domain ◽  
Journal Papers

This paper presents the work done on recommendations of healthcare related journal papers by understanding the semantics of terms from the papers referred by users in past. In other words, user profiles based on user interest within the healthcare domain are constructed from the kind of journal papers read by the users. Multiple user profiles are constructed for each user based on different categories of papers read by the users. The proposed approach goes to the granular level of extrinsic and intrinsic relationship between terms and clusters highly semantically related relevant domain terms where each cluster represents a user interest area. The semantic analysis of terms is done starting from co-occurrence analysis to extract the intra-couplings between terms and then the inter-couplings are extracted from the intra-couplings and then finally clusters of highly related terms are formed. The experiments showed improved precision for the proposed approach as compared to the state-of-the-art technique with a mean reciprocal rank of 0.76.

Web Application for User Profiling

2016 ◽  
Vol 8 (2) ◽  
pp. 44-56
Author(s):  
Taous Iggui ◽  
Hassina Nacer ◽  
Youcef Sklab ◽  
Taklit Ait Radi
Keyword(s):  
Natural Language Processing ◽  
Information Extraction ◽  
Language Processing ◽  
Web Application ◽  
User Profile ◽  
User Profiling ◽  
Good Precision ◽  
User Profiles ◽  
Extraction Techniques ◽  
Novel Approach

User's profiles play an important role when information systems try to meet their needs. This work presents a novel approach to build user profiles. It is based on information extraction techniques and proceeds by iterative steps. The use of different statistic metrics, Natural Language Processing (NLP) techniques and semantic descriptions (ontologies) in the authors' approach, has provided it with a good precision degree when extracting information from texts. This has been demonstrated by an application prototype which is an automatic user profile constructor, using the texts of emails job applications (E recruitment field).

An Overview of Shallow and Deep Natural Language Processing for Ontology Learning

2011 ◽  
pp. 16-37 ◽  
Author(s):  
Amal Zouaq
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Process ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Knowledge Extraction ◽  
The State ◽  
Ontology Learning ◽  
Analysis Methods

This chapter gives an overview over the state-of-the-art in natural language processing for ontology learning. It presents two main NLP techniques for knowledge extraction from text, namely shallow techniques and deep techniques, and explains their usefulness for each step of the ontology learning process. The chapter also advocates the interest of deeper semantic analysis methods for ontology learning. In fact, there have been very few attempts to create ontologies using deep NLP. After a brief introduction to the main semantic analysis approaches, the chapter focuses on lexico-syntactic patterns based on dependency grammars and explains how these patterns can be considered as a step towards deeper semantic analysis. Finally, the chapter addresses the “ontologization” task that is the ability to filter important concepts and relationships among the mass of extracted knowledge.

Semantic Relatedness Estimation using the Layout Information of Wikipedia Articles

2013 ◽  
Vol 7 (2) ◽  
pp. 30-48 ◽  
Author(s):  
Patrick Chan ◽  
Yoshinori Hijikata ◽  
Toshiya Kuramochi ◽  
Shogo Nishida
Keyword(s):  
Information Retrieval ◽  
Word Frequency ◽  
Language Processing ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Low Frequency ◽  
Semantic Relatedness ◽  
Conventional Approach ◽  
Explicit Semantic Analysis

Computing the semantic relatedness between two words or phrases is an important problem in fields such as information retrieval and natural language processing. Explicit Semantic Analysis (ESA), a state-of-the-art approach to solve the problem uses word frequency to estimate relevance. Therefore, the relevance of words with low frequency cannot always be well estimated. To improve the relevance estimate of low-frequency words and concepts, the authors apply regression to word frequency, its location in an article, and its text style to calculate the relevance. The relevance value is subsequently used to compute semantic relatedness. Empirical evaluation shows that, for low-frequency words, the authors’ method achieves better estimate of semantic relatedness over ESA. Furthermore, when all words of the dataset are considered, the combination of the authors’ proposed method and the conventional approach outperforms the conventional approach alone.

scholarly journals Smart E bicycle: an efficient and effective way to greener future

2018 ◽  
Vol 7 (4.5) ◽  
pp. 477
Author(s):  
Harvinder Singh ◽  
Pinky .
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
User Profile ◽  
State Of Charge ◽  
User Profiles ◽  
State Of Health ◽  
Safety Critical ◽  
Multiple User ◽  
Electric Bicycle ◽  
Artificial Intelli

This paper presents and proposes a smart electric bicycle(SeB) leveraging the power of wireless technologies, artificial intelli- gence and cloud computing in order make its user’s experience smooth, safe and enjoyable hence encouraging the user to choose SeB over other modes of transportation. The proposed system introduces an Electric Bicycle connected with a smartphone in one variant or with “smartphone and cloud” in another variant for smart decision-making and efficiency and other related tips for the user. The range of bicycle is predicted based upon the user profile (weight, age etc.), route details (inclinations, distances of al- ternative routes), State of Charge(Soc) and State of Health(SoH) of the battery used. Multiple user profiles and minute details of the route (slope, speed breakers etc.) are captured using sensor like accelerometer and basis on these data smart decisions for pow- er saving and range extensions are made. Also, safety critical and predictive maintenance features are presented.  

Designing a Chat-Bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

2020 ◽  
Vol 01 ◽  
Author(s):  
Radha Guha
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Text Summarization ◽  
The Internet ◽  
Specific Domain ◽  
User Query ◽  
College Information ◽  
Chat Bot

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition

2021 ◽  
pp. 1-12
Author(s):  
Yingwen Fu ◽  
Nankai Lin ◽  
Xiaotian Lin ◽  
Shengyi Jiang
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Neural Models ◽  
Performance Models ◽  
Named Entity ◽  
High Resource ◽  
Benchmark Datasets

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

Characterising Usage of a Multicomponent Digital Intervention to Predict Treatment Outcomes in First-Episode Psychosis: A Cluster Analysis (Preprint)

2021 ◽  
Author(s):  
Shaunagh O'Sullivan ◽  
Lianne Schmaal ◽  
Simon D'Alfonso ◽  
Yara J Toenders ◽  
Lee Valentine ◽  
...  
Keyword(s):  
Young People ◽  
Treatment Outcomes ◽  
Social Functioning ◽  
Negative Symptoms ◽  
User Profile ◽  
First Episode Psychosis ◽  
First Episode ◽  
User Profiles ◽  
Episode Psychosis ◽  
Social Components

BACKGROUND Multicomponent digital interventions offer the potential for tailored and flexible interventions that aim to address high attrition rates and increase engagement, an area of concern in digital mental health. However, increased flexibility in usage makes it difficult to determine which components lead to improved treatment outcomes. OBJECTIVE This study aimed to identify user profiles on Horyzons, an 18-month digital relapse prevention intervention that incorporates therapeutic content and social networking, along with clinical, vocational and peer support, and to examine the predictive value of these user profiles for treatment outcomes. A secondary objective was to compare each user profile with young people receiving treatment as usual (TAU). METHODS Participants comprised 82 young people (16-27 years of age) with access to Horyzons and 84 receiving TAU, recovering from first-episode psychosis. Six-month usage data from the therapy and social networking components of Horyzons were used as features for K-means clustering for joint trajectories to identify user profiles. Social functioning, psychotic symptoms, depression and anxiety were assessed at baseline and six-month follow-up. General linear mixed models were used to examine the predictive value of user profiles for treatment outcomes, and between each user profile with TAU. RESULTS Three user profiles were identified based on system usage metrics including: (a) low usage; (b) maintained usage of social components; and (c) maintained usage of both therapy and social components. The maintained therapy and social group showed improvements in social functioning (F (2, 51) = 3.58; P = .04), negative symptoms (F (2, 51) = 4.45; P = .02) and overall psychiatric symptom severity (F (2, 50) = 3.23; P = .048) compared to the other user profiles. This group also showed improvements in social functioning (F (1, 62) = 4.68; P = .03), negative symptoms (F (1, 62) = 14.61; P = <.001) and overall psychiatric symptom severity (F (1, 63) = 5.66; P = .02) compared to TAU. Conversely, the maintained social group showed increases in anxiety compared to TAU (F (1, 57) = 7.65; P = .01). No differences were found between the low usage group and TAU on treatment outcomes. CONCLUSIONS Continued engagement with both therapy and social components might be key in achieving long-term recovery. Maintained social usage and low usage outcomes were broadly comparable to TAU, emphasizing the importance of maintaining engagement for improved treatment outcomes. Although the social network may be a key ingredient to increase sustained engagement, as users engaged with this more consistently, it should be leveraged as a tool to engage young people with therapeutic content to bring about social and clinical benefits.

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