With the continuous development of computer and network techniques as well as the extensive application of modern means of communications, computer plays an important role in the social life of modern society. It is always associated with a large number of files which are frequently used. Although new computer hardware products provided by various makers help people to solve the problem that have arisen in carrying files, people still face some difficulties in carrying storage devices. This project will partly realize the basic function of the net hard disk. The net hard disk is used to store the files of users on the internet, making it easy for users to carry files and share files with their friends. The uses are able to download, upload, copy, move and delete files and create a new file folder. They can also create, freeze, delete and alter their account. People can retrieve the file they want from the hard disk without anytime at any place.

The purpose of this thesis was the further development of a rule set used in an automatic text simplification system, and the exploration of whether it is possible to improve the performance of a rule based text simplification system by manual training. A first rule set was developed from a thor- ough literature review, and the rule refinement was performed by manually adapting the first rule set to a set of training texts. When there was no more change added to the set of rules, the training was considered to be completed, and the two sets were applied to a test set, for evaluation. This thesis evaluated the performance of a text simplification system as a clas- sification task, by the use of objective metrics: precision and recall. The comparison of the rule sets revealed a clear improvement of the system, since precision increased from 45% to 82%, and recall increased from 37% to 53%. Both recall and precision was improved after training for the ma- jority of the rules, with a few exceptions. All rule types resulted in a higher score on correctness for R2. Automatic text simplification systems target- ing real life readers need to account for qualitative aspects, which has not been considered in this thesis. Future evaluation should, in addition to quantitative metrics such as precision, recall, and complexity metrics, also account for the experience of the reader.

Aligning parallell terms in a parallell corpus can be done by aligning all words and phrases in the corpus and then performing term extraction on the aligned set of word pairs. Alternatively, term extraction in the source and target text can be made separately and then the resulting term candidates can be aligned, forming aligned parallell terms. This thesis describes an implementation of a word aligner that is applied on extracted term candidates in both the source and the target texts. The term aligner uses statistical measures, the tool Giza++ and heuristics in the search for alignments. The evaluation reveals that the best results are obtained when the term alignment relies heavily on the Giza++ tool and Levenshtein heuristic.

In this study, I constructed a named-entity linking system that maps between contextual word embeddings and knowledge graph embeddings to predict correct entities. To establish a named-entity linking system, I first applied named-entity recognition to identify the entities of interest. I then performed candidate generation via locality sensitivity hashing (LSH), where a candidate group of potential entities were created for each identified entity. Afterwards, my named-entity disambiguation component was performed to select the most probable candidate. By concatenating contextual word embeddings and knowledge graph embeddings in my disambiguation component, I present a novel approach to named-entity linking. I conducted the experiments with the Kensho-Derived Wikimedia Dataset and the AIDA CoNLL-YAGO Dataset; the former dataset was used for deployment and the later is a benchmark dataset for entity linking tasks. Three deep learning models were evaluated on the named-entity disambiguation component with different context embeddings. The evaluation was treated as a classification task, where I trained my models to select the correct entity from a list of candidates. By optimizing the named-entity linking through this methodology, this entire system can be used in recommendation engines with high F1 of 86% using the former dataset. With the benchmark dataset, the proposed method is able to achieve F1 of 79%.

Denna studie undersökte hur automatiskt textförenkling upplevs av och hjälper högstadieelever med dyslexi. Deltagarna har fått läsa totalt fyra texter om olika ämnen uppdelat på två tillfällen där ena texten alltid varit förenklad och den andra en originaltext.Efter att läst texterna har deltagarna fått svara på läsförståelsefrågor för att se hur mycketde förstod av texten och sedan delta i en semi-strukturerad intervju för att ta reda på deras upplevelse av texten de hade läst. Resultatet från läsförståelsen delades sedan upp i degrupper som läst samma version av texterna och svaren i de semi-strukturerade intervjuerna analyserades i en tematisk analys. Resultatet visade att ingen större skillnad uppstodmellan texterna utan att båda texterna fungerade lika bra.

Fill-in-the-blank multiple choice questions (MCQs) play an important role in the educational field, but the manual generation of them is quite resource-consuming, so it has gradually turned into an attractive NLP task. Thereinto, question creation itself has become a mainstream NLP research topic, while distractor (wrong alternative) generation (DG) still remains out of the spotlight. Although several studies on distractor generation have been conducted in recent years, there is little previous work on languages other than English. The goal of this thesis is to generate multilingual distractors in Chinese, Arabic, German, and English across domains. The initial step is to construct small-sized multilingual scientific datasets (En, Zh, Ar, and De) and general datasets (Zh and Ar) from scratch. Considering that there are limited multilingual labelled datasets, unsupervised experiments based on WordNet, Word Embedding, transformer-based models, translation methods, and domain adaptation are conducted to generate their corresponding candidate distractors. Finally, the performance of methods is evaluated against our newly-created datasets, where three metrics are applied. Lastly, statistical results show that monolingual transformer-based together with translation-based methods outperform the rest of the approaches for multilingual datasets, except for German, which reaches its highest score only through the translation-based means, and distractor generation in English datasets is the simplest to implement, whereas it is the most difficult in Arabic datasets.

A lot of interaction between humans take place on social media. Groups and communities are sometimes formed both with and without intention. These interactions generate a large quantity of text data. This project aims to detect dissimilarity in discourse between communities on social media using a distributed approach. A data set of tweets was used to test and evaluate the method. Tweets produced from two communities were extracted from the data set. Two Natural Language Processing techniques were used to vectorise the tweets for each community. Namely LIWC, dictionary based on knowledge acquired from professionals in linguistics and psychology, and BERT, an embedding model which uses machine learning to present words and sentences as a vector of decimal numbers. These vectors were then used as representations of the text to measure the similarity of discourse between the communities. Both distance and similarity were measured. It was concluded that none of the combinations of measure or vectorisation method that was tried could detect a dissimilarity in discourse on social media for the sample data set.

Text clustering divides a set of texts into groups, so that texts within each group are similar in content. It may be used to uncover the structure and content of unknown text sets as well as to give new perspectives on known ones. The contributions of this thesis are an investigation of text representation for Swedish and an evaluation method that uses two or more manual categorizations. Text clustering, at least such as it is treated here, is performed using the vector space model, which is commonly used in information retrieval. This model represents texts by the words that appear in them and considers texts similar in content if they share many words. Languages differ in what is considered a word. We have investigated the impact of some of the characteristics of Swedish on text clustering. Since Swedish has more morphological variation than for instance English we have used a stemmer to strip suffixes. This gives moderate improvements and reduces the number of words in the representation. Swedish has a rich production of solid compounds. Most of the constituents of these are used on their own as words and in several different compounds. In fact, Swedish solid compounds often correspond to phrases or open compounds in other languages.In the ordinary vector space model the constituents of compounds are not accounted for when calculating the similarity between texts. To use them we have employed a spell checking program to split compounds. The results clearly show that this is beneficial. The vector space model does not regard word order. We have tried to extend it with nominal phrases in different ways. Noneof our experiments have shown any improvement over using the ordinary model. Evaluation of text clustering results is very hard. What is a good partition of a text set is inherently subjective. Automatic evaluation methods are either intrinsic or extrinsic. Internal quality measures use the representation in some manner. Therefore they are not suitable for comparisons of different representations. External quality measures compare a clustering with a (manual) categorization of the same text set. The theoretical best possible value for a measure is known, but it is not obvious what a good value is -- text sets differ in difficulty to cluster and categorizations are more or less adapted to a particular text set. We describe an evaluation method for cases where a text set has more than one categorization. In such cases the result of a clustering can be compared with the result for one of the categorizations, which we assume is a good partition. We also describe the kappa coefficient as a clustering quality measure in the same setting. / Textklustring delar upp en mängd texter i grupper, så att texterna inom dessa liknar varandra till innehåll. Man kan använda textklustring för att uppdaga strukturer och innehåll i okända textmängder och för att få nya perspektiv på redan kända. Bidragen i denna avhandling är en undersökning av textrepresentationer för svenska texter och en utvärderingsmetod som använder sig av två eller fler manuella kategoriseringar. Textklustring, åtminstonde som det beskrivs här, utnyttjar sig av den vektorrumsmodell, som används allmänt inom området. I denna modell representeras texter med orden som förekommer i dem och texter som har många gemensamma ord betraktas som lika till innehåll. Vad som betraktas som ett ord skiljer sig mellan språk. Vi har undersökt inverkan av några av svenskans egenskaper på textklustring. Eftersom svenska har större morfologisk variation än till exempel engelska har vi tagit bort suffix med hjälp av en stemmer. Detta ger lite bättre resultat och minskar antalet ord i representationen. I svenska används och skapas hela tiden fasta sammansättningar. De flesta delar av sammansättningar används som ord på egen hand och i många olika sammansättningar. Fasta sammansättningar i svenska språket motsvarar ofta fraser och öppna sammansättningar i andra språk. Delarna i sammansättningar används inte vid likhetsberäkningen i vektorrumsmodellen. För att utnyttja dem har vi använt ett rättstavningsprogram för att dela upp sammansättningar. Resultaten visar tydligt att detta är fördelaktigt I vektorrumsmodellen tas ingen hänsyn till ordens inbördes ordning. Vi har försökt utvidga modellen med nominalfraser på olika sätt. Inga av våra experiment visar på någon förbättring jämfört med den vanliga enkla modellen. Det är mycket svårt att utvärdera textklustringsresultat. Det ligger i sakens natur att vad som är en bra uppdelning av en mängd texter är subjektivt. Automatiska utvärderingsmetoder är antingen interna eller externa. Interna kvalitetsmått utnyttjar representationen på något sätt. Därför är de inte lämpliga att använda vid jämförelser av olika representationer. Externa kvalitetsmått jämför en klustring med en (manuell) kategorisering av samma mängd texter. Det teoretiska bästa värdet för måtten är kända, men vad som är ett bra värde är inte uppenbart -- mängder av texter skiljer sig åt i svårighet att klustra och kategoriseringar är mer eller mindre lämpliga för en speciell mängd texter. Vi beskriver en utvärderingsmetod som kan användas då en mängd texter har mer än en kategorisering. I sådana fall kan resultatet för en klustring jämföras med resultatet för en av kategoriseringarna, som vi antar är en bra uppdelning. Vi beskriver också kappakoefficienten som ett kvalitetsmått för klustring under samma förutsättningar. / QC 20101220

Troubleshooting radio devices that power modern mobile networks is currently a manual and labour-intensive process, where speed is crucial to minimize network downtime. Therefore, there is a strong interest in building a retrieval system capable of intelligent search and retrieval in radio logs. To facilitate effective retrieval, several retrievers were evaluated using different configurations. First, a RoBERTa language model was further pretrained on a dataset of unannotated radio logs. Then, a proprietary annotated retrieval dataset named the Event LogRetrieval (ELR) dataset was collected and utilized along with the MS MARCOretrieval dataset for training and evaluating the retrieval models. The evaluation compared different retrieval paradigms for log retrieval; evaluated the impact of further pretraining the language model on log data; and investigated which con-figuration yielded the best performance. The results of the investigation show that the late interaction retrieval paradigm used by the ColBERT model performs best for log retrieval. The results also showed that while further pretraining the language model on logs did improve the representations of log data, it did not improve the performance of the implemented retriever. The investigation into the retrieval datasets showed that fine-tuning on the small ELR dataset is insufficient and that fine-tuning on the larger MS MARCO dataset yielded better performance. The best performance was seen though when first fine-tuning on MSMARCO and then on ELR.

Det svenska språket ska finnas tillgängligt för alla som bor och verkar i Sverige. Därförär det viktigt att det finns lättlästa alternativ för dem som har svårighet att läsa svensktext. Detta arbete bygger vidare på att visa att det är möjligt att skapa ett automatisktomskrivningsprogram som gör texter mer lättlästa. Till grund för arbetet liggerCogFLUX som är ett verktyg för automatisk omskrivning till lätt svenska. CogFLUXinnehåller funktioner för att syntaktiskt skriva om texter till mer lättläst svenska.Omskrivningarna görs med hjälp av omskrivningsregler framtagna i ett tidigare projekt.I detta arbete implementeras ytterligare omskrivningsregler och även en ny modul förhantering av synonymer. Med dessa nya regler och modulen ska arbetet undersöka omdet är det är möjligt att skapa system som ger en mer lättläst text enligt etableradeläsbarhetsmått som LIX, OVIX och Nominalkvot. Omskrivningsreglerna ochsynonymhanteraren testas på tre olika texter med en total lägnd på ungefär hundra tusenord. Arbetet visar att det går att sänka både LIX-värdet och Nominalkvoten signifikantmed hjälp av omskrivningsregler och synonymhanterare. Arbetet visar även att det finnsfler saker kvar att göra för att framställa ett riktigt bra program för automatiskomskrivning till lätt svenska.