Digital Commons Network^™

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 …

The Political Power Of Twitter, James Usher, Pierpaolo Dondio, Lucia Morales

Conference papers

In June 2016, the British voted by 52 per cent to leave the EU, a club the UK joined in 1973. This paper examines Twitter public and political party discourse surrounding the BREXIT withdrawal agreement. In particular, we focus on tweets from four different BREXIT exit strategies known as “Norway”, “Article 50”, the “Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of December 10th 2018 to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis …

English Wikipedia Corpus Chunks, Filip Klubicka, Alfredo Maldonado, Abhijit Mahalunkar, John D. Kelleher

Datasets

This archive contains a collection of language corpora. These are text files that contain samples of text collected from English Wikipedia.

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 …

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 …

Rethinking Global-Regulation: World’S Law Meets Artificial Intelligence, Nachshon Sean Goltz, Addison Cameron-Huff, Giulia Dondoli

Research outputs 2014 to 2021

This article takes a critical look at Machine Translation of legal text, especially global legislation, through the discussion of Global-Regulation, a state of the art online search engine of the world’s legislation in English. Part 2 explains the rationale for an online platform such as Global-Regulation. Part 3 provides a brief account of the history of the development of machine translation, and it describes some of the limits of the use of statistical machine translation for translating legal texts. Part 4 describes Neural Machine Translation (NMT), which is a new generation of machine translation systems. Finally, Parts 5 and 6 …

Towards Modeling Conceptual Dependency Primitives With Image Schema Logic, Jamie C. Macbeth, Dagmar Gromann

Computer Science: Faculty Publications

Conceptual Dependency (CD) primitives and Image Schemas (IS) share a common goal of grounding symbols of natural language in a representation that allows for automated semantic interpretation. Both seek to establish a connection between high-level conceptualizations in natural language and abstract cognitive building blocks. Some previous approaches have established a CD-IS correspondence. In this paper, we build on this correspondence in order to apply a logic designed for image schemas to selected CD primitives with the goal of formally taking account of the CD inventory. The logic draws from Region Connection Calculus (RCC-8), Qualitative Trajectory Calculus (QTC), Cardinal Directions and …

Reports Of The Aaai 2019 Spring Symposium Series, Ioana Baldini, Clark Barrett, Antonio Chella, Carlos Cinelli, David Gamez, Leilani H. Gilpin, Knut Hinkelmann, Dylan Holmes, Takashi Kido, Murat Kocaoglu, William F. Lawless, Alessio Lomuscio, Jamie C. Macbeth, Andreas Martin, Ranjeev Mittu, Evan Patterson, Donald Sofge, Prasad Tadepalli, Keiki Takadama, Shomir Wilson

Computer Science: Faculty Publications

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have …

Linguistic Variation And Anomalies In Comparisons Of Human And Machine-Generated Image Captions, Minyue Dai, Sandra Grandic, Jamie C. Macbeth

Computer Science: Faculty Publications

Describing the content of a visual image is a fundamental ability of human vision and language systems. Over the past several years, researchers have published on major improvements on image captioning, largely due to the development of deep learning systems trained on large data sets of images and human-written captions. However, these systems have major limitations, and their development has been narrowly focused on improving scores on relatively simple “bag-of-words” metrics. Very little work has examined the overall complex patterns of the language produced by image-captioning systems and how it compares to captions written by humans. In this paper, we …

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 Critical Review Of Current Approaches And Practices In Computing Ethics Education, Sophia Farquhar

Dissertations, Master's Theses and Master's Reports

Recent scandals caused by the results of negligent, malicious, or shortsighted software development practices highlight the need for software developers to consider the ethical implications of their work. Computing ethics has historically been a marginalized area within computing disciplines, so educators in these disciplines do not have a common background for teaching the topic. Computing ethics education, although often a required part of coursework, can vary widely in the method of implementation from university to university.

In this report I summarize the insights I gained from interviewing four educators from three different institutions on their pedagogical approaches to computing ethics. …

Machine Reading Comprehension For Multiclass Questions On Thai Corpus, Theerit Lapchaicharoenkit

Chulalongkorn University Theses and Dissertations (Chula ETD)

Previous Thai question answering and machine reading comprehension researches focus on small scale dataset and do not utilize the deep learning approach to build the models. In this research, we develop a Thai machine reading comprehension (MRC) model on Thai MRC dataset provided by NECTEC. This dataset consists of 17,000 question-answer pairs and has two classes of questions, which are factoid and yes-no questions. We use BIDAF as the based MRC architecture. We have performed experiments with 3 different multiclass model designs, which includes special tokens, joint, and cascade model. We also utilize contextual embeddings for Thai language to enhance …

การจำแนกเพศจากชื่อผู้ใช้งานภาษาไทยบนเฟซบุ๊ก, สุพิชชา ยืนยงค์

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์นี้นำเสนอการนำการเรียนรู้ของเครื่องมาประยุกต์ในการจำแนกเพศผู้ใช้งานเฟซบุ๊กโดยใช้เพียงชื่อผู้ใช้งานเท่านั้น ซึ่งข้อมูลส่วนตัวของผู้ใช้งานของโซเชียลเนตเวิร์คมีความสำคัญในการนำมาวิเคราะห์ แต่บางครั้งไม่มีการเปิดเผยข้อมูล เช่น อายุ หรือเพศ โดยการศึกษาส่วนใหญ่มักจะนำเอาข้อความบนเว็บเพจมาวิเคราะห์ แต่การศึกษานี้เลือกใช้ชื่อผู้ใช้งานในการจำแนกเพศ โดยเพศสามารถอนุมานได้จากทั้งชื่อจริงและชื่อแฝงของผู้ใช้งาน โดยงานวิจัยนี้สนใจเฉพาะชื่อที่เป็นภาษาไทย ซึ่งชื่อของคนไทยจะมีรูปแบบที่สามารถแสดงตัวตนความเป็นเพศได้ การรวมกันของแบบจำลองสำหรับการจำแนกเพศจากชื่อผู้ใช้งานภาษาไทยบนเฟซบุ๊กที่แตกต่างกัน ในแต่ละแบบจำลองมีการเรียนรู้โดยใช้วิธีการเรียนรู้แบบจำลองเชิงทำนาย ได้แก่ การจำแนกเพศจากชื่อจริง การจำแนกเพศจากชื่อแฝง การจำแนกชื่อจริงและชื่อแฝง และการจำแนกชื่อทั้งหมด โดยผลการจำแนกทั้งหมดจะถูกรวมในแบบจำลองสุดท้าย เมื่อใช้วิธีนี้แบบจำลองมีความถูกต้องที่ 85.85% ซึ่งได้ผลลัพธ์ที่ดีกว่าเมื่อเปรียบเทียบกับการจำแนกเพศโดยคน ที่มีความถูกต้องที่ 77.03%

Thai Spelling Correction And Word Normalization On Social Text Using A Two-Stage Pipeline With Neural Contextual Attention, Anuruth Lertpiya

Chulalongkorn University Theses and Dissertations (Chula ETD)

Text correction systems (e.g., spell checkers) have been used to improve the quality of computerized text by detecting and correcting errors. However, the task of performing spelling correction and word normalization (text correction) for Thai social media text has remained largely unexplored. In this thesis, we investigated how current text correction systems perform on correcting errors and word variances in Thai social texts and propose a method designed for this task. We have found that currently available Thai text correction systems are insufficiently robust for correcting spelling errors and word variances, while the text correctors designed for English grammatical error …

Fine-Grained Topic Models Using Anchor Words, Jeffrey A. Lund

Theses and Dissertations

Topic modeling is an effective tool for analyzing the thematic content of large collections of text. However, traditional probabilistic topic modeling is limited to a small number of topics (typically no more than hundreds). We introduce fine-grained topic models, which have large numbers of nuanced and specific topics. We demonstrate that fine-grained topic models enable use cases not currently possible with current topic modeling techniques, including an automatic cross-referencing task in which short passages of text are linked to other topically related passages. We do so by leveraging anchor methods, a recent class of topic model based on non-negative matrix …

Using Deep Learning To Forecast Spatiotemporal Crime Patterns, Shuzhan Fan

LSU Doctoral Dissertations

The distributional patterns of crime occurrences are closely related to their spatial, temporal, and environmental contexts. It has been a hot topic for researchers and crime analysts to discover such complex relationships in order to forecast crime, both spatially and temporally. Many factors play a role in the occurrences of crimes. Conventional crime forecasting research has primarily relied on historical crime records and socioeconomic data, while ignoring the rich social media and other environmental context data. The large volume of data requires a more appropriate forecasting framework with the ability to take in massive multimodal data and possibly achieve better …

Classification Of Clinical Narratives Using Convolutional Neural Network, Nikit Rajiv Lonari

Computer Science and Engineering Theses - Archive

Patient safety is a key aspect for good consumer care. When an individual is hospitalized or receives medication the family wants the patient safety to be above all factors. For instance, a drug can do both either cure the disease or perhaps, give rise to an adverse event. A drug administered for an indicated condition has substantial power to reduce or cure a disease and further to prevent it from happening again in the future but at the risk of side effects. At present, there are several methods in patient safety and in particular in the area of signal detection …

Text Mining On Twitter Data To Evaluate Sentiment, Srijanee Niyogi

Computer Science and Engineering Theses - Archive

Social media platforms have been a major part of our daily lives. But with the freedom of expression there is no way one can check whether the posts/tweets/expressions are classified on which polarity. Since Twitter is one of the biggest social platforms for microblogging, hence the experiment was done on this platform. There are several topics that are popular over the internet like sports, politics, finance, technology are chosen as the source of the experiment. These tweets were collected over a span of time for more than 2 months via a cron job. Every tweet can be divided into three …

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 …

The Politeness Package: Detecting Politeness In Natural Language, Michael Yeomans, Alejandro Kantor, Dustin Tingley

The R Journal

This package provides tools to extract politeness markers in English natural language. It also allows researchers to easily visualize and quantify politeness between groups of documents. This package combines and extends prior research on the linguistic markers of politeness (Brown and Levinson, 1987; Danescu-Niculescu-Mizil et al., 2013; Voigt et al., 2017). We demonstrate two applications for detecting politeness in natural language during consequential social interactions— distributive negotiations, and speed dating.

Jsr223: A Java Platform Integration For R With Programming Languages Groovy, Javascript, Jruby, Jython, And Kotlin, Floid R. Gilbert, David B. Dahl

The R Journal

The R package jsr223 is a high-level integration for five programming languages in the Java platform: Groovy, JavaScript, JRuby, Jython, and Kotlin. Each of these languages can use Java objects in their own syntax. Hence, jsr223 is also an integration for R and the Java platform. It enables developers to leverage Java solutions from within R by embedding code snippets or evaluating script f iles. This approach is generally easier than rJava’s low-level approach that employs the Java Native Interface. jsr223’s multi-language support is dependent on the Java Scripting API: an implementation of “JSR-223: Scripting for the Java …

Rcppmsgpack: Messagepack Headers And Interface Functions For R, Travers Ching, Dirk Eddelbuettel

The R Journal

MessagePack, or MsgPack for short, or when referring to the implementation, is an efficient binary serialization format for exchanging data between different programming languages. The RcppMsgPack package provides R with both the MessagePack C++ header files, and the ability to access, create and alter MessagePack objects directly from R. The main driver functions of the R interface are two functions msgpack_pack and msgpack_unpack. The function msgpack_pack serializes Robjects to a raw MessagePack message. The function msgpack_unpack de-serializes MessagePack messages back into R objects. Several helper functions are available to aid in processing and formatting data including msgpack_simplify, msgpack_format and msgpack_map

Perflearner: Learning From Bug Reports To Understand And Generate Performance Test Frames, Xue Han, Tingting Yu, David Lo

Research Collection School Of Computing and Information Systems

Software performance is important for ensuring the quality of software products. Performance bugs, defined as programming errors that cause significant performance degradation, can lead to slow systems and poor user experience. While there has been some research on automated performance testing such as test case generation, the main idea is to select workload values to increase the program execution times. These techniques often assume the initial test cases have the right combination of input parameters and focus on evolving values of certain input parameters. However, such an assumption may not hold for highly configurable real-word applications, in which the combinations …

Towards Automated Domain-Oriented Lexicon Construction And Dimension Reduction For Arabic Sentiment Analysis, Hasan A. Alshahrani

Dissertations

Sentiment analysis is a type of text mining that uses Natural Language Processing (NLP) tools to identify and label opinionated text. There are two main approaches of sentiment analysis: lexicon-based, and statistical approach. In our research, we use the lexicon-based approach because the lexicon contains sentiment words and phrases which are the main linguistic units to express sentiments. More specifically, we work with domain-oriented lexicons as they are more efficient than general ones because the polarity is heavily driven by domains.

Arabic language has a degree of uniqueness that makes it hard to be processed with the available cross-language tools …

Improving The Quality Of Neural Machine Translation Using Terminology Injection, Duane K. Dougal

Theses and Dissertations

Most organizations use an increasing number of domain- or organization-specific words and phrases. A translation process, whether human or automated, must also be able to accurately and efficiently use these specific multilingual terminology collections. However, comparatively little has been done to explore the use of vetted terminology as an input to machine translation (MT) for improved results. In fact, no single established process currently exists to integrate terminology into MT as a general practice, and especially no established process for neural machine translation (NMT) exists to ensure that the translation of individual terms is consistent with an approved terminology collection. …

Induced Hesitant 2-Tuple Linguistic Aggregation Operators With Application In Group Decision Making, Tabasam Rashid, Ismat Beg, Raja N. Jamil

Applications and Applied Mathematics: An International Journal (AAM)

In this article, hesitant 2-tuple linguistic arguments are used to evaluate the group decision making problems which have inter dependent or inter active attributes. Operational laws are developed for hesitant 2-tuple linguistic elements and based on these operational laws hesitant 2- tuple weighted averaging operator and generalized hesitant 2- tuple averaging operator are proposed. Combining Choquet integral with hesitant 2-tuple linguistic information, some new aggregation operators are defined, including the hesitant 2-tuple correlated averaging operator, the hesitant 2-tuple correlated geometric operator and the generalized hesitant 2-tuple correlated averaging operator. These proposed operators successfully manage the correlations among the elements. After …

Making A Good Thing Better: Enhancing Password/Pin-Based User Authentication With Smartwatch, Bing Chang, Yingjiu Li, Qiongxiao Wang, Wen-Tao Zhu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Wearing smartwatches becomes increasingly popular in people’s lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this …

Attention-Based Lstm-Cnns For Uncertainty Identification On Chinese Social Media Texts, Binyang Li, Kaiming Zhou, Wei Gao, Xu Han Han, Linna Zhou

Research Collection School Of Computing and Information Systems

Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the …

On Learning Psycholinguistics Tools For English-Based Creole Languages Using Social Media Data, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The Linguistic Inquiry and Word Count (LIWC) tool is a psycholinguistics tool that has been widely used in both psychology and sociology research, and the LIWC scores derived from user-generated content are known to be good features for personality prediction [1], [2]. LIWC, however, is language specific as it relies on counting the percentage of predefined dictionary words occurring in the content. For content written in English Creoles which are languages based on English, the original English LIWC may not perform optimally due to its lack of words which are only used in the English Creoles. In this paper, we …

Modeling Movement Decisions In Networks: A Discrete Choice Model Approach, Larry Lin Junjie

Dissertations and Theses Collection (Open Access)

In this dissertation, we address the subject of modeling and simulation of agents and their movement decision in a network environment. We emphasize the development of high quality agent-based simulation models as a prerequisite before utilization of the model as an evaluation tool for various recommender systems and policies. To achieve this, we propose a methodological framework for development of agent-based models, combining approaches such as discrete choice models and data-driven modeling.

The discrete choice model is widely used in the field of transportation, with a distinct utility function (e.g., demand or revenue-driven). Through discrete choice models, the movement decision …