Did you hear where the thesis found its ancestors? They were in the "parent-thesis"! This joke, whether you laughed at it or not, contains a fascinating and mysterious quality: humor. Humor is something so incredibly human that if you squint, the two words can even look the same. As such, humor is not often considered something that computers can understand. But, that doesn't mean we won't try to teach it to them. In this thesis, we propose the system Laff-O-Tron to attempt to predict when the audience of a public speech would laugh by looking only at the text of the speech. To do this, we create a corpus of over 1700 TED Talks retrieved from the TED website. We then adapted various techniques used by researchers to identify humor in text. We also investigated features that were specific to our public speaking environment. Using supervised learning, we try to classify if a chunk of text would cause the audience to laugh or not based on these features. We examine the effects of each feature, classifier, and size of the text chunk provided. On a balanced data set, we are able to accurately predict laughter with up to 75% accuracy in our best conditions. Medium level conditions prove to be around 70% accuracy; while our worst conditions result in 66% accuracy. Computers with humor recognition capabilities would be useful in the fields of human computer interaction and communications. Humor can make a computer easier to interact with and function as a tool to check if humor was properly used in an advertisement or speech.

Genealogical records play a crucial role in helping people to discover their lineage and to understand where they come from. They provide a way for people to celebrate their heritage and to possibly reconnect with family they had never considered. However, genealogical records are hard to come by for ordinary people since their information is not always well established in known databases. There often is free form text that describes a person’s life, but this must be manually read in order to extract the relevant genealogical information. In addition, multiple texts may have to be read in order to create an extensive tree. This thesis proposes a novel three part system which can automatically interpret free form text to extract relationships and produce a family tree compliant with GED- COM formatting. The first subsystem builds an extendable database of genealogical records that are systematically extracted from free form text. This corpus provides the tagged data for the second subsystem, which trains a Naı̈ve Bayes classifier to predict relationships from free form text by examining the types of relationships for pairs of entities and their associated feature vectors. The last subsystem accumulates extracted relationships into family trees. When a multiclass Naı̈ve Bayes classifier is used, the proposed system achieves an accuracy of 54%. When binary Naı̈ve Bayes classifiers are used, the proposed system achieves accuracies of 69% for the child to parent relationship classifier, 75% for the spousal relationship classifier, and 73% for the sibling relationship classifier.

The aim of this work is to find out which algorithm is the best at recognizing named entities in e-mail messages. The theoretical part explains the existing tools in this field. The practical part describes the design of two tools specifically designed to create new models capable of recognizing named entities in e-mail messages. The first tool is based on a neural network and the second tool uses a CRF graph model. The existing and newly created tools and their ability to generalize are compared on a subset of e-mail messages provided by Kiwi.com.

The aim of this master’s thesis is analysis of the multidimensional data. Three dimensionality reduction algorithms are introduced. It is shown how to manipulate with text documents using basic methods of natural language processing. The goal of the practical part of the thesis is to process real-world data from the internet forum. Posted messages are transformed to the numerical representation, then to two-dimensional space and visualized. Later on, topics of the messages are discovered. In the last part, a few selected algorithms are compared.

Online store customers generate vast amounts of product and service information through reviews, which are an important source of feedback. This thesis deals with the creation of a system for the analysis of product and shop reviews in the czech language. It describes the current methods of sentiment analysis and builds on current solutions. The resulting system implements automatic data download and their indexing, subsequently sentiment analysis together with text summary in the form of clustering of similar sentences based on vector representation of the text. A graphical user interface in the form of a web page is also included. A review data set with a total of more than six million reviews was created during the semester along with an interface for easy data export.

We explore the use a Latent Dirichlet Allocation (LDA) imitating pseudo-topic-model, based on our original relevance metric, as a tool to facilitate distant annotation of short (often one to two sentence or less) documents. Our exploration manifests as annotating tweets for emotions, this being the current use-case of interest to us, but we believe the method could be extended to any multi-class labeling task of documents of similar length. Tweets are gathered via the Twitter API using "track" terms thought likely to capture tweets with a greater chance of exhibiting each emotional class, 3,000 tweets for each of 26 topics anticipated to elicit emotional discourse. Our pseudo-topic-model is used to produce relevance-ranked vocabularies for each corpus of tweets and these are used to distribute emotional annotations to those tweets not manually annotated, magnifying the number of annotated tweets by a factor of 29. The vector labels the annotators produce for the topics are cascaded out to the tweets via three different schemes which are compared for performance by proxy through the competition of bidirectional-LSMTs trained using the tweets labeled at a distance. An SVM and two emotionally annotated vocabularies are also tested on each task to provide context and comparison.

With the increasing amount of digital data in the form of unstructured text, the importance of natural language processing (NLP) increases. The most suc- cessful technologies of recent years are deep neural networks. This work applies the state-of-the-art methods, namely transfer learning of Bidirectional Encoders Representations from Transformers (BERT), on three Czech NLP tasks: part- of-speech tagging, lemmatization and sentiment analysis. We applied BERT model with a simple classification head on three Czech sentiment datasets: mall, facebook, and csfd, and we achieved state-of-the-art results. We also explored several possible architectures for tagging and lemmatization and obtained new state-of-the-art results in both tagging and lemmatization with fine-tunning ap- proach on data from Prague Dependency Treebank. Specifically, we achieved accuracy 98.57% for tagging, 99.00% for lemmatization, and 98.19% for joint accuracy of both tasks. Best models for all tasks are publicly available. 1

Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts, thereby mitigating the fault propagation to later phases where the same faults are harder to find and fix. The output of an inspection process is list of faults that are present in software requirements specification document (SRS). The artifact author must manually read through the reviews and differentiate between true-faults and false-positives before fixing the faults. The first goal of this research is to automate the detection of useful vs. non-useful reviews. Next, post-inspection, requirements author has to manually extract key problematic topics from useful reviews that can be mapped to individual requirements in an SRS to identify fault-prone requirements. The second goal of this research is to automate this mapping by employing Key phrase extraction (KPE) algorithms and semantic analysis (SA) approaches to identify fault-prone requirements. During fault-fixations, the author has to manually verify the requirements that could have been impacted by a fix. The third goal of my research is to assist the authors post-inspection to handle change impact analysis (CIA) during fault fixation using NL processing with semantic analysis and mining solutions from graph theory. The selection of quality inspectors during inspections is pertinent to be able to carry out post-inspection tasks accurately. The fourth goal of this research is to identify skilled inspectors using various classification and feature selection approaches. The dissertation has led to the development of automated solution that can identify useful reviews, help identify skilled inspectors, extract most prominent topics/keyphrases from fault logs; and help RE author during the fault-fixation post inspection.

The task of opinion mining from product reviews has been achieved by employing rule-based approaches or generative learning models such as hidden Markov models (HMMs). This paper introduced a discriminative model using linear-chain Conditional Random Fields (CRFs) that can naturally incorporate arbitrary, non-independent features of the input without conditional independence among the features or distributional assumptions of inputs. The framework firstly performs part-of-speech (POS) tagging tasks over each word in sentences of review text. The performance is evaluated based on three criteria: precision, recall and F-score. The result shows that this approach is effective for this type of natural language processing (NLP) tasks. Then the framework extracts the keywords associated with each product feature and summarizes into concise lists that are simple and intuitive for people to read.

The elections in South Africa are contested by multiple political parties appealing to a diverse population that comes from a variety of socioeconomic backgrounds. As a result, a rich source of discourse is created to inform voters about election-related content. Two common sources of information to help voters with their decision are news articles and tweets, this study aims to understand the discourse in these two sources using natural language processing. Topic modelling techniques, Latent Dirichlet Allocation and Non- negative Matrix Factorization, are applied to digest the breadth of information collected about the elections into topics. The topics produced are subjected to further analysis that uncovers similarities between topics, links topics to dates and events and provides a summary of the discourse that existed prior to the South African general elections. The primary focus is on the 2019 elections, however election-related articles from 2014 and 2019 were also compared to understand how the discourse has changed. / Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2019. / Computer Science / MIT (Big Data Science) / Unrestricted