Computer Sciences Commons^™

Toward Multi-Label Sentiment Analysis: A Transfer Learning Based Approach, Jie Tao, Xing Fang

Faculty Publications - Information Technology

Sentiment analysis is recognized as one of the most important sub-areas in Natural Language Processing (NLP) research, where understanding implicit or explicit sentiments expressed in social media contents is valuable to customers, business owners, and other stakeholders. Researchers have recognized that the generic sentiments extracted from the textual contents are inadequate, thus, Aspect Based Sentiment Analysis (ABSA) was coined to capture aspect sentiments expressed toward specific review aspects. Existing ABSA methods not only treat the analytical problem as single-label classification that requires a fairly large amount of labelled data for model training purposes, but also underestimate the entity aspects …

Pseudo-Data Generation For Improving Clinical Named Entity Recognition, Jeffrey T. Smith

Theses and Dissertations

One of the primary challenges for clinical Named Entity Recognition (NER) is the availability of annotated training data. Technical and legal hurdles prevent the creation and release of corpora related to electronic health records (EHRs). In this work, we look at the imapct of pseudo-data generation on clinical NER using gazetteering and thresholding utilizing a neural network model. We report that gazetteers can result in the inclusion of proper terms with the exclusion of determiners and pronouns in preceding and middle positions. Gazetteers that had higher numbers of terms inclusive to the original dataset had a higher impact. We also …

Efficient Turkish Tweet Classification System For Crisis Response, Saed Alqaraleh, Merve Işik

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a convolutional neural networks Turkish tweet classification system for crisis response. This system has the ability to classify the present information before or during any crisis. In addition, a preprocessing model was also implemented and integrated as a part of the developed system. This paper presents the first ever Turkish tweet dataset for crisis response, which can be widely used and improve similar studies. This dataset has been carefully preprocessed, annotated, and well organized. It is suitable to be used by all the well-known natural language processing tools. Extensive experimental work, using our produced Turkish tweet dataset …

Automated Labeling Of Terms In Medical Reports In Serbian, Aldina Avdic, Ulfeta Marovac, Dragan Jankovic

Turkish Journal of Electrical Engineering and Computer Sciences

Nowadays, many electronic health reports (EHRs) are stored daily. They consist of the structured part and of an unstructured section written in natural language. Due to the limited time for medical examination, EHRs are short reports which often contain errors and abbreviations. Therefore it is a challenge to process an EHR and extract knowledge from this part of the text for different purposes. This paper compares the results of three proposed methods for automatic labeling of medical terms in unstructured parts of EHRs. All words are categorized as words within the medical domain (symptoms, diagnoses, therapies, anatomy, specialties etc.) and …

Empowering Qualitative Research Methods In Education With Artificial Intelligence, Luca Longo

Conference papers

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches …

Social Media Sentiment Analysis With A Deep Neural Network: An Enhanced Approach Using User Behavioral Information, Ahmed Sulaiman M. Alharbi

Dissertations

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data (including tweet length, spelling errors, abbreviations, and special characters), the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis constitutes a fundamental problem with many interesting applications, such as for Business Intelligence, Medical Monitoring, and National Security. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this research, we propose deep learning based frameworks that …

Supporting Software Architecture Maintenance By Providing Task-Specific Recommendations, Matthias Galster, Christoph Treude, Kelly Blincoe

Research Collection School Of Computing and Information Systems

During software maintenance, developers have different information needs (e.g., to understand what type of maintenance activity to perform, the impact of a maintenance activity and its effort). However, information to support developers may be distributed across various sources. Furthermore, information captured in formal architecture documentation may be outdated. In this paper, we put forward a late breaking idea and outline a solution to improve the productivity of developers by providing task-specific recommendations based on concrete information needs that arise during software maintenance.

Knowledge Base Question Answering With A Matching-Aggregation Model And Question-Specific Contextual Relations, Yunshi Lan, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Making use of knowledge bases to answer questions (KBQA) is a key direction in question answering systems. Researchers have developed a diverse range of methods to address this problem, but there are still some limitations with the existing methods. Specifically, the existing neural network-based methods for KBQA have not taken advantage of the recent “matching-aggregation” framework for the sequence matching, and when representing a candidate answer entity, they may not choose the most useful context of the candidate for matching. In this paper, we explore the use of a “matching-aggregation” framework to match candidate answers with questions. We further make …

Machine Learning-Based Models For Assessing Impacts Before, During And After Hurricane Events, Julie L. Harvey

Electronic Theses and Dissertations

Social media provides an abundant amount of real-time information that can be used before, during, and after extreme weather events. Government officials, emergency managers, and other decision makers can use social media data for decision-making, preparation, and assistance. Machine learning-based models can be used to analyze data collected from social media. Social media data and cloud cover temperature as physical sensor data was analyzed in this study using machine learning techniques. Data was collected from Twitter regarding Hurricane Florence from September 11, 2018 through September 20, 2018 and Hurricane Michael from October 1, 2018 through October 18, 2018. Natural language …

Exploring The Dynamics Of Scientific Research, Shilpa Lakhanpal

Dissertations

Scientific research papers present the research endeavors of numerous scientists around the world, and are documented across multitudes of technical conference proceedings, and other such publications. Given the plethora of such research data, if we could automate the extraction of key interesting areas of research, and provide access to this new information, it would make literature searches incredibly easier for researchers. This in turn could be very useful for them in furthering their research agenda. With this goal in mind, we have endeavored to provide such solutions through our research. Specifically, the focus of our research is to design, analyze …

A Data Driven Approach To Identify Journalistic 5ws From Text Documents, Venkata Krishna Mohan Sunkara

School of Computing: Dissertations, Theses, and Student Research

Textual understanding is the process of automatically extracting accurate high-quality information from text. The amount of textual data available from different sources such as news, blogs and social media is growing exponentially. These data encode significant latent information which if extracted accurately can be valuable in a variety of applications such as medical report analyses, news understanding and societal studies. Natural language processing techniques are often employed to develop customized algorithms to extract such latent information from text.

Journalistic 5Ws refer to the basic information in news articles that describes an event and include where, when, who, what and why …

Music Mood Classification Using Convolutional Neural Networks, Revanth Akella

Master's Projects

Grouping music into moods is useful as music is migrating from to online streaming services as it can help in recommendations. To establish the connection between music and mood we develop an end-to-end, open source approach for mood classification using lyrics. We develop a pipeline for tag extraction, lyric extraction, and establishing classification models for classifying music into moods. We investigate techniques to classify music into moods using lyrics and audio features. Using various natural language processing methods with machine learning and deep learning we perform a comparative study across different classification and mood models. The results infer that features …

Chatbots With Personality Using Deep Learning, Susmit Gaikwad

Master's Projects

Natural Language Processing (NLP) requires the computational modelling of the complex relationships of the syntax and semantics of a language. While traditional machine learning methods are used to solve NLP problems, they cannot imitate the human ability for language comprehension. With the growth in deep learning, these complexities within NLP are easier to model, and be used to build many computer applications. A particular example of this is a chatbot, where a human user has a conversation with a computer program, that generates responses based on the user’s input. In this project, we study the methods used in building chatbots, …

Question Answering With Textual Sequence Matching, Shuohang Wang

Dissertations and Theses Collection (Open Access)

Question answering (QA) is one of the most important applications in natural language processing. With the explosive text data from the Internet, intelligently getting answers of questions will help humans more efficiently collect useful information. My research in this thesis mainly focuses on solving question answering problem with textual sequence matching model which is to build vectorized representations for pairs of text sequences to enable better reasoning. And our thesis consists of three major parts.

In Part I, we propose two general models for building vectorized representations over a pair of sentences, which can be directly used to solve the …

Untapped Potential Of Clinical Text For Opioid Surveillance, Amy L. Olex, Tamas Gal, Majid Afshar, Dmitriy Dligach, Niranjan Karnik, Travis Oakes, Brihat Sharma, Meng Xie, Bridget T. Mcinnes, Julian Solway, Abel Kho, William Cramer, F. Gerard Moeller

Wright Center for Clinical and Translational Research Works

Accurate surveillance is needed to combat the growing opioid epidemic. To investigate the potential volume of missed opioid overdoses, we compare overdose encounters identified by ICD-10-CM codes and an NLP pipeline from two different medical systems. Our results show that the NLP pipeline identified a larger percentage of OOD encounters than ICD-10-CM codes. Thus, incorporating sophisticated NLP techniques into current diagnostic methods has the potential to improve surveillance on the incidence of opioid overdoses.

Test: A Terminology Extraction System For Technology Related Terms, Murhaf Hossari, Soumyabrata Dev, John Kelleher

Conference papers

Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified …

Reorganize Your Blogs: Supporting Blog Re-Visitation With Natural Language Processing And Visualization, Shuo Niu, D. Scott Mccrickard, Timothy L. Stelter, Alan Dix, G. Don Taylor

Computer Science

Temporally-connected personal blogs contain voluminous textual content, presenting challenges in re-visiting and reflecting on experiences. Other data repositories have benefited from natural language processing (NLP) and interactive visualizations (VIS) to support exploration, but little is known about how these techniques could be used with blogs to present experiences and support multimodal interaction with blogs, particularly for authors. This paper presents the effect of reorganization—reorganizing the large blog set with NLP and presenting abstract topics with VIS—to support novel re-visitation experiences to blogs. The BlogCloud tool, a blog re-visitation tool that reorganizes blog paragraphs around user-searched keywords, implements reorganization and similarity-based …

Application Of Boolean Logic To Natural Language Complexity In Political Discourse, Austin Taing

Theses and Dissertations--Computer Science

Press releases serve as a major influence on public opinion of a politician, since they are a primary means of communicating with the public and directing discussion. Thus, the public’s ability to digest them is an important factor for politicians to consider. This study employs several well-studied measures of linguistic complexity and proposes a new one to examine whether politicians change their language to become more or less difficult to parse in different situations. This study uses 27,500 press releases from the US Senate between 2004–2008 and examines election cycles and natural disasters, namely hurricanes, as situations where politicians’ language …

The D&D Sorting Hat: Predicting Dungeons And Dragons Characters From Textual Backstories, Joseph C. Macinnes

Senior Independent Study Theses

Dungeons and Dragons is a tabletop roleplaying game which focuses heavily on character interaction and creating narratives. The current state of the game's character creation process often bogs down new players in decisions related to game mechanics, not a character's identity and personality. This independent study investigates the use of machine learning and natural language processing to make these decisions for a player based on their character's backstory - the textual biography or description of a character. The study presents a collection of existing characters and uses these examples to create a family of models capable of predicting a character's …

Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia

Research Collection School Of Computing and Information Systems

Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …

A Hybrid Sentiment Analysis Method For Turkish, Buket Erşahi̇n, Özlem Aktaş, Deni̇z Kilinç, Mustafa Erşahi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a hybrid methodology for Turkish sentiment analysis, which combines the lexicon-based and machine learning (ML)-based approaches. On the lexicon-based side, we use a sentiment dictionary that is extended with a synonyms lexicon. Besides this, we tackle the classification problem with three supervised classifiers, naive Bayes, support vector machines, and J48, on the ML side. Our hybrid methodology combines these two approaches by generating a new lexicon-based value according to our feature generation algorithm and feeds it as one of the features to machine learning classifiers. Despite the linguistic challenges caused by the morphological structure of Turkish, the …

Automatic Concept Identification Of Software Requirements In Turkish, Fatma Bozyi̇ği̇t, Özlem Aktaş, Deni̇z Kilinç

Turkish Journal of Electrical Engineering and Computer Sciences

Software requirements include description of the features for the target system and express the expectations of users. In the analysis phase, requirements are transformed into easy-to-understand conceptual models that facilitate communication between stakeholders. Although creating conceptual models using requirements is mostly implemented manually by analysts, the number of models that automate this process has increased recently. Most of the models and tools are developed to analyze requirements in English, and there is no study for agglutinative languages such as Turkish or Finnish. In this study, we propose an automatic concept identification model that transforms Turkish requirements into Unified Modeling Language …

Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles

Computer Science Faculty Publications

We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …

Comparison Mining From Text, Maksim Tkachenko

Dissertations and Theses Collection (Open Access)

Online product reviews are important factors of consumers' purchase decisions. They invade more and more spheres of our life, we have reviews on books, electronics, groceries, entertainments, restaurants, travel experiences, etc. More than 90 percent of consumers read online reviews before they purchase products as reported by various consumers surveys. This observation suggests that product review information enhances consumer experience and helps them to make better-informed purchase decisions. There is an enormous amount of online reviews posted on e-commerce platforms, such as Amazon, Apple, Yelp, TripAdvisor. They vary in information and may be written with different experiences and preferences.

If …

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

Detecting Saliency By Combining Speech And Object Detection In Indoor Environments, Kiran Thapa

Boise State University Theses and Dissertations

Describing scenes such as rooms, city streets, or routes, is a very common human task that requires the ability to identify and describe the scene sufficiently for a hearer to develop a mental model of the scene. When people talk about such scenes, they mention some objects of the scene at the exclusion of others. We call the mentioned objects salient objects as people consider them noticeable or important in comparison to other non-mentioned objects. In this thesis, we look at saliency of visual scenes and how visual saliency informs what can and should be said about a scene when …

Chrono: A System For Normalizing Temporal Expressions, Amy L. Olex, Luke G. Maffey, Nicholas Morton, Bridget T. Mcinnes

Computer Science Publications

The Chrono System: Chrono is a hybrid rule-based and machine learning system written in Python and built from the ground up to identify temporal expressions in text and normalizes them into the SCATE schema. Input text is preprocessed using Python’s NLTK package, and is run through each of the four primary modules highlighted here. Note that Chrono does not remove stopwords because they add temporal information and context, and Chrono does not tokenize sentences. Output is an Anafora XML file with annotated SCATE entities. After minor parsing logic adjustments, Chrono has emerged as the top performing system for SemEval 2018 …

Assesing Completeness Of Solvency And Financial Condition Reports Through The Use Of Machine Learning And Text Classification, Ruairí Nugent

Dissertations

Text mining is a method for extracting useful information from unstructured data through the identification and exploration of large amounts of text. It is a valuable support tool for organisations. It enables a greater understanding and identification of relevant business insights from text. Critically it identifies connections between information within texts that would otherwise go unnoticed. Its application is prevalent in areas such as marketing and political science however, until recently it has been largely overlooked within economics. Central banks are beginning to investigate the benefits of machine learning, sentiment analysis and natural language processing in light of the large …

Validation Of Tagging Suggestion Models For A Hotel Ticketing Corpus, Bojan Bozic, Andre Rios, Sarah Jane Delany

Conference papers

This paper investigates methods for the prediction of tags on a textual corpus that describes hotel staff inputs in a ticketing system. The aim is to improve the tagging process and find the most suitable method for suggesting tags for a new text entry. The paper consists of two parts: (i) exploration of existing sample data, which includes statistical analysis and visualisation of the data to provide an overview, and (ii) evaluation of tag prediction approaches. We have included different approaches from different research fields in order to cover a broad spectrum of possible solutions. As a result, we have …

Measuring Goal Similarity Using Concept, Context And Task Features, Vahid Eyorokon

Browse all Theses and Dissertations

Goals can be described as the user's desired state of the agent and the world and are satisfied when the agent and the world are altered in such a way that the present state matches the desired state. For physical agents, they must act in the world to alter it in a series of individual atomic actions. Traditionally, agents use planning to create a chain of actions each of which altering the current world state and yielding a new one until the final action yields the desired goal state. Once this goal state has been achieved, the goal is said …