Att utveckla en sofistikerad chatbotlösning kräver stora mängder textdata för att kunna anpassalösningen till en specifik domän. Att manuellt skapa en komplett uppsättning textdata, specialanpassat för den givna domänen och innehållandes ett stort antal varierande meningar som en människa kan tänkas yttra, är ett enormt tidskrävande arbete. För att kringgå detta tillämpas dataaugmentering för att generera mer data utifrån en mindre uppsättning redan existerande textdata. Softronic AB vill undersöka alternativa strategier för dataaugmentering med målet att eventuellt ersätta den nuvarande lösningen med en mer vetenskapligt underbyggd sådan. I detta examensarbete har prototypmodeller utvecklats för att jämföra och utvärdera effekten av olika textaugmenteringsstrategier. Resultatet av genomförda experiment med prototypmodellerna visar att augmentering genom synonymutbyten med en domänanpassad synonymordlista, presenterade märkbart förbättrade effekter på förmågan hos en NLU-modell att korrekt klassificera data, gentemot övriga utvärderade strategier. Vidare indikerar resultatet att ett samband föreligger mellan den strukturella variationsgraden av det augmenterade datat och de tillämpade språkparens semantiska likhetsgrad under tillbakaöversättningar. / Developing a sophisticated chatbot solution requires large amounts of text data to be able to adapt the solution to a specific domain. Manually creating a complete set of text data, specially adapted for the given domain, and containing a large number of varying sentences that a human conceivably can express, is an exceptionally time-consuming task. To circumvent this, data augmentation is applied to generate more data based on a smaller set of already existing text data. Softronic AB wants to investigate alternative strategies for data augmentation with the aim of possibly replacing the current solution with a more scientifically substantiated one. In this thesis, prototype models have been developed to compare and evaluate the effect of different text augmentation strategies. The results of conducted experiments with the prototype models show that augmentation through synonym swaps with a domain-adapted thesaurus, presented noticeably improved effects on the ability of an NLU-model to correctly classify data, compared to other evaluated strategies. Furthermore, the result indicates that there is a relationship between the structural degree of variation of the augmented data and the applied language pair's semantic degree of similarity during back-translations.

Förståelse för naturligt språk, språk som har utvecklats av människan ex. talspråk eller teckenspråk, är en del av språkteknik. Det är ett brett ämnesområde där utvecklingen har gått fram i snabb takt senaste 20 åren. En bidragande faktor till denna utveckling är framgångarna med neurala nätverk som är en matematisk modell inspirerad av biologiska hjärnor. Förståelse för naturligt språk används inom många områden där det krävs att applikationer förstår innebörden av textinmatning. Exempel på applikationer som använder förståelse för naturligt språk är Google translate, Googles sökmotor och rättstavningsfunktionen i textredigerarprogram.   A Great Thing AB har utvecklat applikationen Thing Launcher. Thing Launcher är en applikation som hanterar andra applikationer med hjälp av användarens olika kriterier i samband mobilens olika funktionaliteter som; väder, geografisk position, tid mm. Ett exempel kan vara att användaren vill att Spotify ska spela en specifik låt när användaren kommer hem, eller att en taxi ska vara på plats när användaren anländer till en geografisk position.  I dagsläget styr man Thing Launcher med hjälp av textinmatningar. A Great Thing AB behöver hjälp att ta en prototyp på en motor med förståelse för naturligt språk som kan styras av både textinmatning och röstinmatning. Motorn ska användas i applikationen Thing Launcher. Med skalbarhet menas att motorn ska kunna utvecklas, att nya funktioner och applikationer ska kunna läggas till, samtidigt som systemet ska kunna vara i drift och att prestandan påverkas så lite som möjligt.   Detta examensarbete har som syfte att undersöka vilka algoritmer som är lämpliga för att bygga en skalbar motor med förståelse av naturligt språk. Utifrån detta utveckla en prototyp. En litteraturstudie gjordes mellan dolda Markovmodeller och neurala nätverk. Resultatet visade att neurala nätverk var överlägset i förståelse av naturligt språk. Flera typer av neurala nätverk finns implementerade i TensorFlow och den är mycket flexibelt med sitt bredda utbud av kompatibla mobila enheter, vilket nyttar utvecklingen med det modulära aspekten och därför valdes detta som ramverk för att utveckla prototypen. De två viktigaste komponenterna i prototypen bestod av Command tagger, som ska kunna identifiera vilken applikation som användaren vill styra och NER tagger, som ska identifiera vad användaren vill att applikationen ska utföra. För att mäta träffsäkerheten utfördes det två tester, en för respektive tagger, flera gånger som mätte hur ofta komponenterna gissade rätt efter varje träningsrunda. Varje träningsrunda bestod av att komponenterna fick tiotusentals meningar som de fick gissa på följt av facit för att ge feedback. Med hjälp av feedback kunde komponenterna anpassas för hur de agerar i framtiden i samma situation. Command tagger gissade rätt 94 procent av gångerna och Ner tagger gissade rätt 96 procent av gångerna efter de sista träningsrundorna. I prototypen användes Androids inbyggda mjukvara för taligenkänning. Det är en funktion som omvandlar ljudvågor till text. En serverbaserad lösning med REST applikationsgränssnitt utvecklades för att göra motorn skalbar.   Resultatet visar att fungerande prototyp som kan vidareutvecklas till en skalbar motor för naturligt språk. / Natural Language Understanding is a field that is part of Natural Language Processing. Big improvements have been made in the broad field of Natural Language Understanding during the past two decades. One big contribution to this is improvement is Neural Networks, a mathematical model inspired by biological brains. Natural Language Understanding is used in fields that require deeper understanding by applications. Google translate, Google search engine and grammar/spelling check are some examples of applications requiring deeper understanding. Thing Launcher is an application developed by A Great Thing AB. Thing Launcher is an application capable of managing other applications with different parameters. Some examples of parameters the user can use are geographic position and time. The user can as an example control what song will be played when you get home or order an Uber when you arrive to a certain destination. It is possible to control Thing Launcher today by text input. A Great Thing AB needs help developing a prototype capable of understanding text input and speech. The meaning of scalable is that it should be possible to develop, add functions and applications with as little impact as possible on up time and performance of the service. A comparison of suitable algorithms, tools and frameworks has been made in this thesis in order research what it takes to develop a scalable engine with the natural language understanding and then build a prototype from this gathered information. A theoretical comparison was made between Hidden Markov Models and Neural Networks. The results showed that Neural Networks are superior in the field of natural language understanding. The tests made in this thesis indicated that high accuracy could be achieved using neural networks. TensorFlow framework was chosen because it has many different types of neural network implemented in C/C++ ready to be used with Python and alsoand for the wide compatibility with mobile devices.  The prototype should be able to identify voice commands. The prototype has two important components called Command tagger, which is going to identify which application the user wants to control and NER tagger, which is the going to identify what the user wants to do. To calculate the accuracy, two types of tests, one for each component, was executed several times to calculate how often the components guessed right after each training iteration. Each training iteration consisted of giving the components thousands of sentences to guess and giving them feedback by then letting them know the right answers. With the help of feedback, the components were molded to act right in situations like the training. The tests after the training process resulted with the Command tagger guessing right 94% of the time and the NER tagger guessing right 96% of the time. The built-in software in Android was used for speech recognition. This is a function that converts sound waves to text. A server-based solution with REST interface was developed to make the engine scalability. This thesis resulted with a working prototype that can be used to further developed into a scalable engine.

Semantic Web and Linked Data community is now the reality of the future of the Web. The standards and technologies defined in this field have opened a strong pathway towards a new era of knowledge management and representation for the computing world. The data structures and the semantic formats introduced by the Semantic Web standards offer a platform for all the data and knowledge providers in the world to present their information in a free, publicly available, semantically tagged, inter-linked, and machine-readable structure. As a result, the adaptation of the Semantic Web standards by data providers creates numerous opportunities for development of new applications which were not possible or, at best, hardly achievable using the current state of Web which is mostly consisted of unstructured or semi-structured data with minimal semantic metadata attached tailored mainly for human-readability. This dissertation tries to introduce a framework for effective analysis of the Semantic Web data towards the development of solutions for a series of related applications. In order to achieve such framework, Wikipedia is chosen as the main knowledge resource largely due to the fact that it is the main and central dataset in Linked Data community. In this work, Wikipedia and its Semantic Web version DBpedia are used to create a semantic graph which constitutes the knowledgebase and the back-end foundation of the framework. The semantic graph introduced in this research consists of two main concepts: entities and topics. The entities act as the knowledge items while topics create the class hierarchy of the knowledge items. Therefore, by assigning entities to various topics, the semantic graph presents all the knowledge items in a categorized hierarchy ready for further processing. Furthermore, this dissertation introduces various analysis algorithms over entity and topic graphs which can be used in a variety of applications, especially in natural language understanding and knowledge management fields. After explaining the details of the analysis algorithms, a number of possible applications are presented and potential solutions to these applications are provided. The main themes of these applications are entity detection, topic identification, and context acquisition. To demonstrate the efficiency of the framework algorithms, some of the applications are developed and comprehensively studied by providing detailed experimental results which are compared with appropriate benchmarks. These results show how the framework can be used in different configurations and how different parameters affect the performance of the algorithms.

Semantic Web and Linked Data community is now the reality of the future of the Web. The standards and technologies defined in this field have opened a strong pathway towards a new era of knowledge management and representation for the computing world. The data structures and the semantic formats introduced by the Semantic Web standards offer a platform for all the data and knowledge providers in the world to present their information in a free, publicly available, semantically tagged, inter-linked, and machine-readable structure. As a result, the adaptation of the Semantic Web standards by data providers creates numerous opportunities for development of new applications which were not possible or, at best, hardly achievable using the current state of Web which is mostly consisted of unstructured or semi-structured data with minimal semantic metadata attached tailored mainly for human-readability. This dissertation tries to introduce a framework for effective analysis of the Semantic Web data towards the development of solutions for a series of related applications. In order to achieve such framework, Wikipedia is chosen as the main knowledge resource largely due to the fact that it is the main and central dataset in Linked Data community. In this work, Wikipedia and its Semantic Web version DBpedia are used to create a semantic graph which constitutes the knowledgebase and the back-end foundation of the framework. The semantic graph introduced in this research consists of two main concepts: entities and topics. The entities act as the knowledge items while topics create the class hierarchy of the knowledge items. Therefore, by assigning entities to various topics, the semantic graph presents all the knowledge items in a categorized hierarchy ready for further processing. Furthermore, this dissertation introduces various analysis algorithms over entity and topic graphs which can be used in a variety of applications, especially in natural language understanding and knowledge management fields. After explaining the details of the analysis algorithms, a number of possible applications are presented and potential solutions to these applications are provided. The main themes of these applications are entity detection, topic identification, and context acquisition. To demonstrate the efficiency of the framework algorithms, some of the applications are developed and comprehensively studied by providing detailed experimental results which are compared with appropriate benchmarks. These results show how the framework can be used in different configurations and how different parameters affect the performance of the algorithms.

This diploma thesis deals with building a chatbot for the MojiLidi system, which helps to find specialists and companies providing their services and products. Chatbot is a type of interface that is controlled by a natural language conversation. Thus, users do not need to know the meaning of buttons or forms and for them it is enough just to say request. The work describes the main parts of chatbot such as understanding the natural language, managing the dialogue, creating responses, communication channel and the conversational user interface. Consequently, such a conversation agent is created and integrated into the MojiLidi web interface from where it helps users in 4 domains.

In this study we gather customer reviews from Prisjakt, a Swedish price comparison site, with the goal to study the relationship between review and rating, known as sentiment analysis. The purpose of the study is to evaluate three different supervised machine learning models on a fine-grained dependent variable representing the review rating. For classification, a binary and multinomial model is used with the one-versus-one strategy implemented in the Support Vector Machine, with a linear kernel, evaluated with F1, accuracy, precision and recall scores. We use Support Vector Regression by approximating the fine-grained variable as continuous, evaluated using MSE. Furthermore, three models are evaluated on a balanced and unbalanced dataset in order to investigate the effects of class imbalance. The results show that the SVR performs better on unbalanced fine-grained data, with the best fine-grained model reaching a MSE 4.12, compared to the balanced SVR (6.84). The binary SVM model reaches an accuracy of 86.37% and weighted F1 macro of 86.36% on the unbalanced data, while the balanced binary SVM model reaches approximately 80% for both measures. The multinomial model shows the worst performance due to the inability to handle class imbalance, despite the implementation of class weights. Furthermore, results from feature engineering shows that SVR benefits marginally from certain regex conversions, and tf-idf weighting shows better performance on the balanced sets compared to the unbalanced sets.

Automatic understanding of stories is a long-time goal of artificial intelligence and natural language processing research communities. Stories literally explain the human experience. Understanding our stories promotes the understanding of both individuals and groups of people; various cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told –by narrators– in linguistic bundles of words called narratives. My work has given computers awareness of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a non-trivial task, because people rarely tell one story at a time. People don’t specifically announce when we are starting or stopping our stories: We interrupt each other. We tell stories within stories. Before my work, computers had no awareness of narrative boundaries, essentially where stories begin and end. My programs can extract narrative boundaries from novels and short stories with an F1 of 0.65. Before this I worked on teaching computers to identify which paragraphs of text have story content, with an F1 of 0.75 (which is state of the art). Additionally, I have taught computers to identify the narrative point of view (POV; how the narrator identifies themselves) and diegesis (how involved in the story’s action is the narrator) with F1 of over 0.90 for both narrative characteristics. For the narrative POV, diegesis, and narrative level extractors I ran annotation studies, with high agreement, that allowed me to teach computational models to identify structural elements of narrative through supervised machine learning. My work has given computers the ability to find where stories begin and end in raw text. This allows for further, automatic analysis, like extraction of plot, intent, event causality, and event coreference. These tasks are impossible when the computer can’t distinguish between which stories are told in what spans of text. There are two key contributions in my work: 1) my identification of features that accurately extract elements of narrative structure and 2) the gold-standard data and reports generated from running annotation studies on identifying narrative structure.

Cloud services are one of the fast expanding fields of today. Companies such as Amazon, Google, Microsoft and IBM offer these cloud services in various forms. As this field progresses, the natural question occurs ”What can you do with the technology today?”. The technology offers scalability for hardware usage and user demands, that is attractive to developers and companies. This thesis tries to examine the applicability of cloud services, by combining it with the question: ”Is it possible to make an automated thesis examiner?” By narrowing down the services to IBM Watson web services, this thesis main question reads ”Is it possible to make an automated thesis examiner using IBM Watson?”. Hence the goal of this thesis was to create an automated thesis examiner. The project used a modified version of Bunge’s technological research method. Where amongst the first steps, a definition of an software thesis examiner for student theses was created. Then an empirical study of the Watson services, that seemed relevant from the literature study, proceeded. These empirical studies allowed a deeper understanding about the services’ practices and boundaries. From these implications and the definition of a software thesis examiner for student theses, an idea of how to build and implement an automated thesis examiner was created. Most of IBM Watson’s services were thoroughly evaluated, except for the service Machine Learning, that should have been studied further if the time resources would not have been depleted. This project found the Watson web services useful in many cases but did not find a service that was well suited for thesis examination. Although the goal was not reached, this thesis researched the Watson web services and can be used to improve understanding of its applicability, and for future implementations that face the provided definition. / Molntjänster är ett av de områden som utvecklas snabbast idag. Företag såsom Amazon, Google, Microsoft och IBM tillhandahåller dessa tjänster i flera former. Allteftersom utvecklingen tar fart, uppstår den naturliga frågan ”Vad kan man göra med den här tekniken idag?”. Tekniken erbjuder en skalbarhet mot använd hårdvara och antalet användare, som är attraktiv för utvecklare och företag. Det här examensarbetet försöker svara på hur molntjänster kan användas genom att kombinera det med frågan ”Är det möjligt att skapa en automatiserad examensarbetesrapportsgranskare?”. Genom att avgränsa undersökningen till IBM Watson molntjänster försöker arbetet huvudsakligen svara på huvudfrågan ”Är det möjligt att skapa en automatiserad examensarbetesrapportsgranskare med Watson molntjänster?”. Därmed var målet med arbetet att skapa en automatiserad examensarbetesrapportsgranskare. Projektet följde en modifierad version av Bunge’s teknologiska undersökningsmetod, där det första steget var att skapa en definition för en mjukvaruexamensarbetesrapportsgranskare följt av en utredning av de Watson molntjänster som ansågs relevanta från litteratur studien. Dessa undersöktes sedan vidare i empirisk studie. Genom de empiriska studierna skapades förståelse för tjänsternas tillämpligheter och begränsningar, för att kunna kartlägga hur de kan användas i en automatiserad examensarbetsrapportsgranskare. De flesta tjänster behandlades grundligt, förutom Machine Learning, som skulle behövt vidare undersökning om inte tidsresurserna tog slut. Projektet visar på att Watson molntjänster är användbara men inte perfekt anpassade för att granska examensarbetesrapporter. Även om inte målet uppnåddes, undersöktes Watson molntjänster, vilket kan ge förståelse för deras användbarhet och framtida implementationer för att möta den skapade definitionen.

SOTA language models have demonstrated remarkable capabilities in tackling NLP tasks they have not been explicitly trained on – given a few demonstrations of the task (few-shot learning), or even none at all (zero-shot learning). The purpose of this Master’s thesis has been to investigate practical aspects and potential applications of zero/few-shot learning in the context of text classification. This includes topics such as combined usage with active learning, automated data labeling, and interpretability. Two different methods for zero/few-shot learning have been investigated, and the results indicate that:  • Active learning can be used to marginally improve few-shot performance, but it seems to be mostly beneficial in settings with very few samples (e.g. less than 10). • Zero-shot learning can be used produce reasonable candidate labels for classes in a dataset, given knowledge of the classification task at hand.  • It is difficult to trust the predictions of zero-shot text classification without access to a validation dataset, but IML methods such as saliency maps could find usage in debugging zero-shot models. / Ledande språkmodeller har uppvisat anmärkningsvärda förmågor i att lösa NLP-problem de inte blivit explicit tränade på – givet några exempel av problemet (few-shot learning), eller till och med inga alls (zero-shot learning). Syftet med det här examensarbetet har varit att undersöka praktiska aspekter och potentiella tillämpningar av zero/few-shot learning inom kontext av textklassificering. Detta inkluderar kombinerad användning med aktiv inlärning, automatiserad datamärkning, och tolkningsbarhet. Två olika metoder för zero/few-shot learning har undersökts, och resultaten indikerar att: • Aktiv inlärning kan användas för att marginellt förbättra textklassificering med few-shot learning, men detta verkar vara mest fördelaktigt i situationer med väldigt få datapunkter (t.ex. mindre än 10). • Zero-shot learning kan användas för att hitta lämpliga etiketter för klasser i ett dataset, givet kunskap om klassifikationsuppgiften av intresse. • Det är svårt att lita på robustheten i textklassificering med zero-shot learning utan tillgång till valideringsdata, men metoder inom tolkningsbar maskininlärning såsom saliency maps skulle kunna användas för att felsöka zero-shot modeller.

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