Natural Language Processing

In the last twenty years, many different approaches to deal with Computer-Interpretable clinical Guidelines (CIGs) have been developed, each one proposing its own representation formalism (mostly based on the Task-Network Model) execution engine. We propose META-GLARE a shell for easily defining new CIG systems. Using META-GLARE, CIG system designers can easily define their own systems (basically by defining their representation language), with a minimal programming effort. META-GLARE is thus a flexible and powerful vehicle for research about CIGs, since it supports easy and fast prototyping of new CIG systems.

The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This is due to the advances in neural network architectures, increase of computing power and the availability of diverse labeled datasets, which deliver pre-trained, highly accurate models. These tasks are generally focused on tagging common entities, but domain-specific use-cases require tagging custom entities which are not part of the pre-trained models. This can be solved by either fine-tuning the pre-trained models, or by training custom models. The main challenge lies in obtaining reliable labeled training and test datasets, and manual labeling would be a highly tedious task.
In this paper we present PharmKE, a text analysis platform focused on the pharmaceutical domain, which applies deep learning through several stages for thorough semantic analysis of pharmaceutical articles. It performs text classification using state-of-the-art transfer learning models, and thoroughly integrates the results obtained through a proposed methodology. The methodology is used to create accurately labeled training and test datasets, which are then used to train models for custom entity labeling tasks, centered on the pharmaceutical domain. The obtained results are compared to the fine-tuned BERT and BioBERT models trained on the same dataset. Additionally, the PharmKE platform integrates the results obtained from named entity recognition tasks to resolve co-references of entities and analyze the semantic relations in every sentence, thus setting up a baseline for additional text analysis tasks, such as question answering and fact extraction. The recognized entities are also used to expand the knowledge graph generated by DBpedia Spotlight for a given pharmaceutical text.

We describe a visualization tool that can be used to view the change in meaning of words over time. The tool makes use of existing (static) word embedding datasets together with a timestamped n-gram cor- pus to create temporal word embeddings.

This paper discusses the interaction of vocal music skill development with phonemic awareness (the ability
to hear and manipulate phonemes, the smallest units of language) and fluency training in kindergarten and
grade one language arts instruction. The phonemic awareness and fluency activities provided in language
arts curricula are often music related: songs, chants and rhymes. Research is discussed regarding auditory
processing related to vocal production; developmentally appropriate singing, prosody (the intentional
grouping of words into phrases), and music perception as it relates to reading achievement. The
pedagogical strategies necessary for developmentally appropriate modeling in both music and language arts
instruction are discussed.

7th International Conference on Artificial Intelligence and Applications (AI 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference
looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

9
th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2021)
provides a forum for researchers who address this issue and to present their work in a peerreviewed forum. Authors are solicited to contribute to the conference by submitting articles that
illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.
Authors are solicited to contribute to this conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the areas of Artificial Intelligence & applications

Natural Resource
perspectives
Number 51, March 2000
This series is published by ODI, an independent non-profit policy research institute, with financial
support from the Department for International Development (formerly the Overseas Development
Administration). Opinions expressed do not necessarily reflect the views of either ODI or DFID. DFID Department for
International
Development
Introduction: Putting poverty at the heart of
the tourism agenda
Lack of focus on poverty within the tourism agenda
The International Development Target of halving the
proportion of people living in extreme poverty by 2015 has
been widely adopted. A number of prominent development
agencies, including the UK’s Department for International
Development (DFID), are developing sustainable livelihoods
approaches in response to these targets.
In the tourism sector, national governments and donors
have generally aimed to promote private sector investment,
macro-economic growth and foreign exchange earnings,
without specifically taking the needs and opportunities of
the poor into account in tourism development (i.e. what we
term here ‘pro-poor tourism’). Donor-supported tourism
master plans focus on creating infrastructure, stimulating
private investment and attracting international tourists.
Investors are often international companies and local élites,
whose profits are generally repatriated abroad or to
metropolitan centres. Links with the local economy are often
weak, with the possible exception of employment.
Since the mid-1980s, interest in ‘green’ tourism, eco-tourism
and community tourism has grown rapidly among decisionmakers,
practitioners and advocates. All of these focus on
the need to ensure that tourism does not erode the
environmental and cultural base on which it depends. But
these generally do not consider the full range of impacts on
the livelihoods of the poor.
The current challenge for governments and donors in
tourism development is to respond to changes in broader
development thinking, by developing strategies to enhance
impacts of tourism on the poor. Recent research in India,
Indonesia, Namibia, Nepal, the Philippines, Uganda, Zambia
and Zimbabwe helps to shed light on the issues involved.
Can tourism really be pro-poor?
Tourism is a complex industry driven by the private sector,
and often by large international companies. Governments
have relatively few instruments to influence this sector,
particularly in developing countries where fiscal and planning
instruments for capturing non-commercial benefits are
generally weak.
Nevertheless, as a sector for pro-poor economic growth,
tourism has several advantages:
• The consumer comes to the destination, thereby providing
opportunities for selling additional goods and services.
• Tourism is an important opportunity to diversify local
economies. It can develop in poor and marginal areas
with few other export and diversification options. Remote
Box 1 Significance of tourism in poor countries
In 1997 developing countries received 30.5% of world
international tourist arrivals, compared with 24% in 1988 (WTO,
1998). International tourism is significant (over 2% of GDP or 5%
of exports) or growing (i.e. by at least 50% in 1990–7) in almost
half of the 48 low income countries, and in virtually all the 53
low income and middle income countries. Among the 12 countries
that are home to 80% of the world’s poor, tourism is significant or
growing in all but one.
Problems of definition
These statistics only cover some economic aspects of international
tourism. Macro-economic data generally only capture arrivals and
foreign exchange receipts associated with international tourism.
There are two problems with this:
• They do not capture domestic tourism, nor do they disaggregate
regional tourism, both of which are significant and growing in
Asia, Africa and South America, and often important markets
for the poor.
• Foreign exchange receipts do not accurately reflect the
economic contribution of tourism. In addition to ‘core’ services
of accommodation and transport, the tourism-related economy
also involves food and drinks, supplies to hotels, local transport
and attractions, guiding, handicrafts and souvenirs. Tourismrelated
services are particularly important for expanding
participation by the poor.
PRO-POOR TOURISM: PUTTING POVERTY AT THE HEART OF THE
TOURISM AGENDA
Caroline Ashley, Charlotte Boyd and Harold Goodwin

This study aims at providing a possible model for linguistic representation, whose structure is able to describe the effects on sign’s indeterminacy (vagueness, ambiguities) and the dynamic nature of the system. In order to display the different ways of conceiving a linguistic system, various versions of the idea of "giving an order to the structures", that build up a theory, is taken into account: the Cartesian theory of generative grammar, the non-Euclidean one of hypertext networks, the complexity in genetic epistemology and in Jean Piaget’s cognitive development theory, and finally the theory of linguistic change by the language biologist Eric Lenneberg.

Scopo di questo studio è il tentativo di ipotizzare un modello di rappresentazione linguistica, la cui struttura sia capace di descrivere gli effetti dell'indeterminatezza del segno (vaghezza, ambiguità) e la dinamicità del sistema. Per mostrare differenti possibilità di concepire astrattamente un sistema linguistico, si considerano versioni diverse del concetto di “ordine delle strutture”: il sistema cartesiano nella grammatica generativa, la geometria non euclidea nelle reti ipertestuali, la (teoria della )complessità nella teoria dello sviluppo psicogenetico di Jean Piaget e nella teoria del mutamento del biolinguista Eric Lenneberg.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Within this article we use the metaphor of the “Research Onion” (Saunders et al., 2012: 128) to illustrate how these final elements (the core of the research onion) need to be considered in relation to other design elements (the outer layers of the research onion).  It is the researcher’s understandings and associated decisions in relation to these outer layers that provide the context and boundaries within which data collection techniques and analysis procedures will be selected. 

Please note, this is the published version and has been uploaded with permission from Karen Moxom (ANLP)

Please note, with Pearson's permission I have uploaded the proofs of chapter 4 for the 7th edition of Research Methods for Business Students (published in August 2015)  to academia.edu.  This contains the latest version of the research onionademia.edu.  The direct link is: https://www.academia.edu/13016419/Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development_

Communities are at risk from extreme events and natural disasters that can lead to dangerous situations for residents. Improving resilience by helping people learn how to better prepare for, recover from, and adapt to disasters is critical to reduce the impacts of these extreme events. This project presents an intelligent system, Flood AI, designed to improve societal preparedness for flooding by providing a knowledge engine that uses voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding. The knowledge engine uses flood ontology to connect user input to relevant knowledge discovery channels on flooding by developing a data acquisition and processing framework using environmental observations, forecast models, and knowledge bases. The framework's communication channels include web-based systems, agent-based chatbots, smartphone applications, automated web workflows, and smart home devices, opening the knowledge discovery for flooding to many unique use cases.

Literature Based Discovery (LBD) is a process that searches for hidden and important connections among information embedded in published literature. Employing techniques from Information Retrieval and Natural Language Processing, LBD has potential for widespread application yet is currently implemented primarily in the medical domain. This article examines several published LBD systems, comparing their descriptions of domain and input data, techniques

Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes’ (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.

Around the world, the use of the Internet and social media has increased exponentially, and they have become an integral part of daily life. It allows people to share their thoughts, feelings, and ideas with their loved ones through the Internet and social media. But with social networking sites becoming more popular, cyberbullying is on the rise. Using technology as a medium to bully someone is known as Cyberbullying. The Internet can be a source of abusive and harmful content and cause harm to others. Social networking sites provide a great medium for harassment, bullies, and youngsters who use these sites are vulnerable to attacks. Bullying can have long-term effects on adolescents' ability to socialize and build lasting friendships Victims of cyberbullying often feel humiliated. social media users often can hide their identity, which helps misuse the available features. The use of offensive language has become one of the most popular issues on social networking. Text containing any form of abusive conduct that displays acts intended to hurt others is offensive language. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and sometimes forces them to commit suicide. The purpose of this project is to develop a technique that is effective to detect and avoid cyberbullying on social networking sites we are using Natural Language Processing and other machine learning algorithms. The dataset that we used for this project was collected from Kaggle, it contains data from Twitter that is then labeled to train the algorithm. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed Model for cyberbullying dataset shows that Logistic Regression performs better and achieves good accuracy than SVM, Ransom forest, Naive-Bayes, and Xgboost algorithm.

We argue for a compositional semantics grounded in a strongly typed ontology that reflects our commonsense view of the world and the way we talk about it. Assuming such a structure we show that the semantics of various natural language phenomena may become nearly trivial.

Named entity recognition (NER) is one of the fundamental tasks in natural-language processing (NLP). Though the combination of different classifiers has been widely applied in several well-studied languages, this is the first time this method has been applied to Vietnamese. In this article, we describe how voting techniques can improve the performance of Vietnamese NER. By combining several state-of-the-art machine-learning algorithms using voting strategies, our final result outperforms individual algorithms and gained an F-measure of 89.12. A detailed discussion about the challenges of NER in Vietnamese is also presented.

Scene text recognition brings various new challenges occurs in recent years. Detecting and recognizing text in scenes entails some of the equivalent problems as document processing, but there are also numerous novel problems to face for recognizing text in natural scene images. Recent research in these regions has exposed several promise but present is motionless much effort to be entire in these regions. Most existing techniques have focused on detecting horizontal or near-horizontal texts. In this paper, we propose a new scheme which detects texts of arbitrary directions in natural scene images. Our algorithm is equipped with two sets of characteristics specially designed for capturing both the natural characteristics of texts using MSER regions using Otsu method. To better estimate our algorithm and compare it with other existing algorithms, we are using existing MSRA Dataset, ICDAR Dataset, and our new dataset, which includes various texts in various real-world situations. Experiments results on these standard datasets and the proposed dataset shows that our algorithm compares positively with the modern algorithms when using horizontal texts and accomplishes significantly improved performance on texts of random orientations in composite natural scenes images.

Abstract. Soft-cardinality spectra (SC spectra) is a new method of approximation for text strings in linear time, which divides text strings into character q-grams of different sizes. The method allows simultaneous use of weighting at term and q-gram levels. SC spectra in combination with resemblance coefficients allows the construction of a family of text similarity functions that only use the surface information of the texts and weights obtained in the same text collection. These similarity measures can be used in various tasks of natural ...

Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/ Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topicbased reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of (https://wordnet.princeton.edu) WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the (https://github.com/ alimuttaleb/semantictaxonomy/blob/master/mc30.txt) MC30 dataset in Word-Net and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96.

Word sense disambiguation (WSD) is the problem of determining the sense of a multi-sense word in a given context. It is one of the important processes needed for natural language processing applications. Inductive Logic Programming (ILP) is the area of artificial intelligence which contains machine learning and logic programming. It aims to build first- order theories from examples and background knowledge, which are also represented by first- order clauses. The richness of first order logic employed in ILP can hopefully provide advantages for NLP applications such as WSD. In this study, a corpus-based WSD application is improved by using ILP.

Many times people use a single word with multiple senses, which provides a different meaning based on the sentence in which it has been used. So, the main goal of the work is to disambiguate an ambiguous word that has been located in certain sentences. Thus, it helps in exploring the actual sense of the word that it means. This process is itself named as Word Sense Disambiguation (WSD) which is an essential and ongoing subject in NLP. However, the role of domain is very helpful in exploring the actual sense of an ambiguous word. There are a number of approaches to WSD which takes a wider semantic space of ambiguous words into account [10]. This semantic space can be represented as a specific domain, task or application [10]. The domain information which is one among them is more advantageous in the process of disambiguation, thus, the work explores the role of the domain in the disambiguation process. The work also includes a score allotment to all the senses of an ambiguous word based on the semantic relation of it with the ambiguous word. Later, this helps in obtaining the actual sense of the word. Keywords: Disambiguation, natural language processing, word sense I. INTRODUCTION In NLP, ambiguity has become a barrier to human language understanding. The best solution to overcome this barrier is the Word Sense Disambiguation process. For instance, consider an example word 'bank' which has two senses. One sense of the word is "financial reservoir" and another sense of it is "a river edge". Now consider the following two statements. "Willows lined the bank of the stream" and "I went to the bank to get a home loan" To a human, the difference between these two senses of the word 'bank' will be clearly understandable as it means 'river bank' in the first sentence and a 'financial reservoir' in the second sentence. But this is not the same in case of a machine. Thus, it needs a different solution. There are different solutions of WSD that have been proposed. This can be generally divided into supervised and knowledge-based approaches. In comparison of these two approaches, the knowledge-based approach has gained a rapid development while compared to others in recent years [12]. Also, the availability of abundant information from a different knowledge resources has narrowed the gap between these two approaches [12]. Hence, the use of a knowledge-based approach has been considered as a useful one in our work. The main idea of this approach is to make use of WordNet and a semantic space such as specific domains to a greater extent to obtain the actual sense of the ambiguous word. Thus, the main purpose of our work is to make use of the domain information as a best possible way to explore the actual meaning of an ambiguous word that has been used in a sentence. There is a hypothesis that the domain information provides a powerful way to establish a semantic relation among the word senses [11]. Thus, it can be used in a profitable way in the process of disambiguation. We can refer to the domain, as a set of words that has a strong semantic relation between them. Thus, an approach of using domain information in obtaining the actual sense of an ambiguous word makes sense. Here, the basic prediction that we assumed to achieve the goal was to make use of a distinct score allotment for each of the senses that comes under the semantic space of domain of ambiguous word. This score allotment is not a random one, rather the sense that is closer to the word in a particular sentence will be allotted with a highest score while the remaining senses will be allotted in a decreasing manner accordingly. This prediction gave a way to attain the result later. Also, this approach helps in less time consumption than the normal disambiguation process. Therefore, the efficiency of the project will also be increased. II. LITERATURE SURVEY S.G Kolte and S.G Bhirud approaches is to Word sense disambiguation using wordnet domains, here they used wordnet as database to define domains and the words in the given sentence is taken as parameter which helps to detect domain by domain-oriented text analysis. For this they go through unsupervised method, and they trying to disambiguate nouns first using pos tag and getting results but here the drawback is that this approach is failed for a word having more than one sense in a same domain [1]. A Fully Unsupervised Word sense disambiguation system using dependency knowledge on specific domain is proposed in the year 2010, here they developed a fully unsupervised system using domain specific knowledge and this system performs above the first sense baseline. they showed that wsd can be achieve in unsupervised method without get compromised with supervised approach by using easily available unannotated text from internet and other sources and get a good result [2].

There is a number of machine learning algorithms for recognizing Arabic characters. In this paper, we investigate a range of strategies for multiple machine learning algorithms for the task of Arabic characters recognition, where we are faced with imperfect and dimensionally variable input characters. We show two different strategies to combining multiple machine learning algorithms: manual backoff strategry and ensemble learning strategy. We show the performance of using individual algorithms and combined algorithms on recognizing Arabic characters. Experimental results show that combined confidence-based strategies can produce more accurate results than each algorithm produces by itself and even the ones exhibited by the majority voting combination.

Abstract: This paper briefly surveys three of the main resources for word sense disambiguation that are currently in use œ WordNet, FrameNet, and Levin classes œ and proposes an alternative approach, focusing on verbs and their valencies. This new approach does not attempt to ...

Most research in text classification to date has used a “bag of words” representation in which each feature corresponds to a single word. This paper examines some alternative ways to represent text based on syntactic and semantic relationships between words (phrases, synonyms ...

In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP's preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of-Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.