To paraphrase an old bromide, "you are what you consume." Consumers derive their sense of self through products, brands, performances and a host of other meaning-laden materials that they consume. The marketing literature has long recognized possessions as an extension of the self-concept. Although hundreds of studies have examined the linkage between consumption and the self, surprisingly few have examined a related phenomenon - the relationship between the self-concept and word of mouth (WOM). A handful of studies have demonstrated the use of WOM to enhance the consumer's self-image, but most extant research focuses on how the act of engaging in WOM is used to build the self-concept. To date there has not been an extensive examination of the process by which WOM transfers the meaning of a product, brand, advertisement or narrative from one consumer to another as part of identity construction. This dissertation attempts to answer the following research questions: 1. Do self-concept and identity motivate consumers to engage in electronic word-of-mouth (eWOM)? 2. Is there a conceptual model to represent the effects of message characteristics, product/brand characteristics and individual personality differences on the self-enhancement value of eWOM and resulting eWOM behaviors? A conceptual model was proposed and, using an experimental research design, hypotheses were tested using structural equation modeling (SEM). The results suggest that self-concept and identity indeed motivate consumers to engage in eWOM, and a number of brand and message traits comprise eWOM's self-enhancement value. This dissertation both contributes to the theoretical understanding of eWOM and assists managers in developing marketing strategy. The use of WOM for identity construction remains an understudied area in marketing when compared to the consumption of products as self-expression. This research provides suggestions for practitioners in harnessing the potential of eWOM as a marketing strategy through message development and targeting of lucrative segments with viral campaigns.

This thesis has attempted to determine if there are differences, concerning adverbial placement, between the sentences of left- and right-handed writers. To make this determination, I have statistically analyzed compositions of eight graduate students (four left-handed and four right-), and two left-handed published authors' (Lewis Carroll's and Mark Twains) private correspondence.

Research in natural language processing (NLP) focuses recently on the development of learned language models called word embedding models like word2vec, fastText, and BERT. Pre-trained on large amounts of unstructured text in natural language, those embedding models constitute a rich source of common knowledge in the domain of the text used for the training. In the NLP community, significant improvements are achieved by using those models together with deep neural network models. To support applications to benefit from word embeddings, we extend the capabilities of traditional relational database systems, which are still by far the most common DBMSs but only provide limited text analysis features. Therefore, we implement (a) novel database operations involving embedding representations to allow a database user to exploit the knowledge encoded in word embedding models for advanced text analysis operations. The integration of those operations into database query language enables users to construct queries using novel word embedding operations in conjunction with traditional query capabilities of SQL. To allow efficient retrieval of embedding representations and fast execution of the operations, we implement (b) novel search algorithms and index structures for approximated kNN-Joins and integrate those into a relational database management system. Moreover, we investigate techniques to optimize embedding representations of text values in database systems. Therefore, we design (c) a novel context adaptation algorithm. This algorithm utilizes the structured data present in the database to enrich the embedding representations of text values to model their context-specific semantic in the database. Besides, we provide (d) support for selecting a word embedding model suitable for a user's application. Therefore, we developed a data processing pipeline to construct a dataset for domain-specific word embedding evaluation. Finally, we propose (e) novel embedding techniques for pre-training on tabular data to support applications working with text values in tables. Our proposed embedding techniques model semantic relations arising from the alignment of words in tabular layouts that can only hardly be derived from text documents, e.g., relations between table schema and table body. In this way, many applications, which either employ embeddings in supervised machine learning models, e.g., to classify cells in spreadsheets, or through the application of arithmetic operations, e.g., table discovery applications, can profit from the proposed embedding techniques.:1 INTRODUCTION 1.1 Contribution 1.2 Outline 2 REPRESENTATION OF TEXT FOR NATURAL LANGUAGE PROCESSING 2.1 Natural Language Processing Systems 2.2 Word Embedding Models 2.2.1 Matrix Factorization Methods 2.2.2 Learned Distributed Representations 2.2.3 Contextualize Word Embeddings 2.2.4 Advantages of Contextualize and Static Word Embeddings 2.2.5 Properties of Static Word Embeddings 2.2.6 Node Embeddings 2.2.7 Non-Euclidean Embedding Techniques 2.3 Evaluation of Word Embeddings 2.3.1 Similarity Evaluation 2.3.2 Analogy Evaluation 2.3.3 Cluster-based Evaluation 2.4 Application for Tabular Data 2.4.1 Semantic Search 2.4.2 Data Curation 2.4.3 Data Discovery 3 SYSTEM OVERVIEW 3.1 Opportunities of an Integration 3.2 Characteristics of Word Vectors 3.3 Objectives and Challenges 3.4 Word Embedding Operations 3.5 Performance Optimization of Operations 3.6 Context Adaptation 3.7 Requirements for Model Recommendation 3.8 Tabular Embedding Models 4 MANAGEMENT OF EMBEDDING REPRESENTATIONS IN DATABASE SYSTEMS 4.1 Integration of Operations in an RDBMS 4.1.1 System Architecture 4.1.2 Storage Formats 4.1.3 User-Defined Functions 4.1.4 Web Application 4.2 Nearest Neighbor Search 4.2.1 Tree-based Methods 4.2.2 Proximity Graphs 4.2.3 Locality-Sensitive Hashing 4.2.4 Quantization Techniques 4.3 Applicability of ANN Techniques for Word Embedding kNN-Joins 4.4 Related Work on kNN Search in Database Systems 4.5 ANN-Joins for Relational Database Systems 4.5.1 Index Architecture 4.5.2 Search Algorithm 4.5.3 Distance Calculation 4.5.4 Optimization Capabilities 4.5.5 Estimation of the Number of Targets 4.5.6 Flexible Product Quantization 4.5.7 Further Optimizations 4.5.8 Parameter Tuning 4.5.9 kNN-Joins for Word2Bits 4.6 Evaluation 4.6.1 Experimental Setup 4.6.2 Influence of Index Parameters on Precision and Execution Time 4.6.3 Performance of Subroutines 4.6.4 Flexible Product Quantization 4.6.5 Accuracy of the Target Size Estimation 4.6.6 Performance of Word2Bits kNN-Join 4.7 Summary 5 CONTEXT ADAPTATION FOR WORD EMBEDDING OPTIMIZATION 5.1 Related Work 5.1.1 Graph and Text Joint Embedding Methods 5.1.2 Retrofitting Approaches 5.1.3 Table Embedding Models 5.2 Relational Retrofitting Approach 5.2.1 Data Preparation 5.2.2 Relational Retrofitting Problem 5.2.3 Relational Retrofitting Algorithm 5.2.4 Online-RETRO 5.3 Evaluation Platform: Retro Live 5.3.1 Functionality 5.3.2 Interface 5.4 Evaluation 5.4.1 Datasets 5.4.2 Training of Embeddings 5.4.3 Machine Learning Models 5.4.4 Evaluation of ML Models 5.4.5 Run-time Measurements 5.4.6 Online Retrofitting 5.5 Summary 6 MODEL RECOMMENDATION 6.1 Related Work 6.1.1 Extrinsic Evaluation 6.1.2 Intrinsic Evaluation 6.2 Architecture of FacetE 6.3 Evaluation Dataset Construction Pipeline 6.3.1 Web Table Filtering and Facet Candidate Generation 6.3.2 Check Soft Functional Dependencies 6.3.3 Post-Filtering 6.3.4 Categorization 6.4 Evaluation of Popular Word Embedding Models 6.4.1 Domain-Agnostic Evaluation 6.4.2 Evaluation of a Single Facet 6.4.3 Evaluation of an Object Set 6.5 Summary 7 TABULAR TEXT EMBEDDINGS 7.1 Related Work 7.1.1 Static Table Embedding Models 7.1.2 Contextualized Table Embedding Models 7.2 Web Table Embedding Model 7.2.1 Preprocessing 7.2.2 Text Serialization 7.2.3 Encoding Model 7.2.4 Embedding Training 7.3 Applications for Table Embeddings 7.3.1 Table Union Search 7.3.2 Classification Tasks 7.4 Evaluation 7.4.1 Intrinsic Evaluation 7.4.2 Table Union Search Evaluation 7.4.3 Table Layout Classification 7.4.4 Spreadsheet Cell Classification 7.5 Summary 8 CONCLUSION 8.1 Summary 8.2 Directions for Future Work BIBLIOGRAPHY LIST OF FIGURES LIST OF TABLES A CONVEXITY OF RELATIONAL RETROFITTING B EVALUATION OF THE RELATIONAL RETROFITTING HYPERPARAMETERS

Digitaliseringen har senaste åren påverkat företagsverksamhet, deras kommunikation med kunderna samt hantering av marknadsföringen. Bibehållning av konkurrenskraft inom detaljhandelns har blivit tuffare med allt bredare användning av Webb 2.0 samt sociala medier. Både kunderna och företag ser högt värde i relationer och direkt kommunikation. Marknadsföring men även strategier har gått från de traditionella formerna till allt mer digitaliserade. Denna studie kommer genom en fallspecifik undersökning av fyra företag inom detaljhandeln undersöka vilken kunskap samt åtgärder har dessa företag använt för att förbättra kommunikationsstrategi samt deras försäljning och vilka resultat har det gett. Studien är av kvalitativ karaktär och baseras därmed på resultat från empiriinsamling i form av intervjuer som kommer stödjas med relevant teori i form av tidigare studier och litteratur. / Digitization has in recent years affected companies' operations, their communication with customers and the management of marketing. Maintaining competitiveness in retail has become tougher with the ever-widening use of Web 2.0 and social media. Both customers and companies see high value in relationships and direct communication. Marketing but also strategies have gone from the traditional forms to increasingly digitized. This study will, through a case-specific survey of four companies in the retail trade, examine what knowledge and measures these companies have used to improve communication strategy and their sales and what results it has given. The study is of a qualitative nature and is based on results from empirical collection in the form of interviews that will be supported with relevant theory in the form of previous studies and literature.

A problem with automatic tagging and lexical analysis is that it is never 100 % accurate. In order to arrive at better figures, one needs to study the character of what is left untagged by automatic taggers. In this paper untagged residue outputted by the automatic analyser SWETWOL (Karlsson 1992) at Helsinki is studied. SWETWOL assigns tags to words in Swedish texts mainly through dictionary lookup. The contents of the untagged residue files are described and discussed, and possible ways of solving different problems are proposed. One method of tagging residual output is proposed and implemented: the left-stripping method, through which untagged words are bereaved their left-most letters, searched in a dictionary, and if found, tagged according to the information found in the said dictionary. If the stripped word is not found in the dictionary, a match is searched in ending lexica containing statistical information about word classes associated with that particular word form (i.e., final letter cluster, be this a grammatical suffix or not), and the relative frequency of each word class. If a match is found, the word is given graduated tagging according to the statistical information in the ending lexicon. If a match is not found, the word is stripped of what is now its left-most letter and is recursively searched in a dictionary and ending lexica (in that order). The ending lexica employed in this paper are retrieved from a reversed version of Nusvensk Frekvensordbok (Allén 1970), and contain endings of between one and seven letters. The contents of the ending lexica are to a certain degree described and discussed. The programs working according to the principles described are run on files of untagged residual output. Appendices include, among other things, LISP source code, untagged and tagged files, the ending lexica containing one and two letter endings and excerpts from ending lexica containing three to seven letters.

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Arbetet undersöker vad som påverkar miljömärkningarnas legitimitet ur ettkonsumentperspektiv, om det finns skillnader i förtroende för företagens egnamiljömärkningar och organisationers miljömärkningar samt hur förtroendet förmiljömärkningar påverkar konsumenternas betalningsvilja. Tidigare studier har belyst attförtroende är avgörande för att miljömärkningarna ska tjäna sitt syfte samtidigt som utbudetav miljömärkningar ökar vilket skapar förvirring och osäkerhet bland konsumenter. Ettannat problem är att livsmedel som märkts med miljömärkningar från organisationer harstriktare krav och är tredjepartscertifierade medan företag som märker livsmedel med egnamiljömärkningar kan ändra sina standarder, vilket kan påverka miljömärkningens legitimitet.För att undersöka miljömärkningens legitimitet har studien tillämpat en kvalitativgruppintervjumetod där åtta konsumenter intervjuades. Studien har avgränsats till tvåsvenska miljömärkningar; ICAs egna varumärke I love eco och miljömärket KRAV.Resultatet från undersökningen tyder på att miljömärkningarnas legitimitet har en betydanderoll för konsumenters köpbeslut. En legitim miljömärkning ansågs enligt konsumenternavara när de följer kraven, har tredjepartscertifiering och när priset motsvarar kvaliteten.Faktorerna ovan visade även ha en betydande roll för konsumenternas betalningsvilja.Skillnad i förtroende för varumärket I love eco och KRAV berodde på kunskapsbrist, då Ilove eco ansågs mindre trovärdig trots att den uppfyller EUs förordning och KRAVs reglerför ekologisk produktion. Det som var mest avgörande för miljömärkningarnas legitimitet ärkunskap om miljömärkningarna, då det råder stor kunskapsbrist kring dem. / This bachelor thesis examines what influence the legitimacy of eco-labels from a consumerperspective, if there is a difference in trust in companies' own eco-labels and organizationaleco-labels and how trust in eco-labels affects consumers' willingness to pay. Previous studieshave highlighted that trust is crucial for eco-labels to serve their purpose at the same time asthe number of eco-labels increases and creates confusion and uncertainty among consumers.Another problem is that foods labelled with organizational eco-labels have stricterrequirements and are third-party certified, while companies that label foods with their owneco-labels can regulate their standards, which affect the legitimacy of the eco-label. Toinvestigate the legitimacy of eco-labelling, the study applied a qualitative group interviewmethod in which a total of eight consumers were interviewed. The study has been limited tothe two Swedish eco-labels ICA's own brand I love eco and KRAV. The results from thesurvey indicate that the legitimacy of eco-labels has a significant role to play in consumerpurchasing decisions. According to consumers, a legitimate eco-label was one that complywith the requirements and third-party certification and when the price corresponds to thequality. These factors above also proved to play a significant role in the consumer'swillingness to pay. The difference in trust in I love eco and KRAV was due to a lack ofknowledge, as I love eco was considered less credible despite complying with the EUregulation and KRAV's rules for organic production. What has been noted to be most crucialfor the legitimacy of eco-labels is knowledge within the eco-labels, as there is a great lack ofknowledge among the consumers surveyed.

Foreign words, such as names, locations or sometimes entire phrases, are a problem for any system that is meant to convert graphemes to phonemes (g2p; i.e.converting written text into phonetic transcription). In this thesis, we investigate both rule-based and neural methods of phonetically transcribing English words found in Icelandic text, taking into account the rules and constraints of how foreign phonemes can be mapped into Icelandic phonology. We implement a rule-based system by compiling grammars into finite-state transducers. In deciding on which rules to include, and evaluating their coverage, we use a list of the most frequently-found English words in a corpus of Icelandic text. The output of the rule-based system is then manually evaluated and corrected (when needed) and subsequently used as data to train a simple bidirectional LSTM g2p model. We train models both with and without length and stress labels included in the gold annotated data. Although the scores for neither model are close to the state-of-the-art for either Icelandic or English, both our rule-based system and LSTM model show promising initial results and improve on the baseline of simply using an Icelandic g2p model, rule-based or neural, on English words. We find that the greater flexibility of the LSTM model seems to give it an advantage over our rule-based system when it comes to modeling certain phenomena. Most notable is the LSTM’s ability to more accurately transcribe relations between graphemes and phonemes for English vowel sounds. Given there does not exist much previous work on g2p transcription specifically handling English words within the Icelandic phonological constraints and it remains an unsolved task, our findings present a foundation for the development of further research, and contribute to improving g2p systems for Icelandic as a whole.

The aim of this thesis has been to analyze German light verb constructions with the light verb leisten and their translations into Swedish. From the identified types containing a nominalization + leisten as parts of a light verb construction, three construction types were examined more closely, namely Beitrag leisten, Arbeit leisten and Hilfe leisten. These light verb constructions showed a higher representation in the material and were hence selected for a further in-depth study. As for the translations into Swedish, three main translation strategies were identified: word-for-word translations, full-verb replacements, and paraphrases/omissions.  The results show that paraphrasing is the preferred strategy, although the result is quite even between all the strategies. The results also show that each of the three constructions preferred one each of the three translation strategies, which opens for further studies in this area. One explanation for this is, relating to the semantic meaning of the nouns and to their flexible characteristics into Swedish, that nouns with a narrower semantic meaning are more inclined to be translated word-for-word, but also depending on whether the nominalization has a function as a direct object in the sentence, is modified by an adjective, or is merely part of a predicative.

Att företag använder sig av influencers som ett marknadsföringsverktyg på sociala medier blir allt vanligare att se. Precis som det engelska ursprungsordet influence betyder så handlar influencer marketing om att varumärken använder personer med stora nätverk av följare för att påverka andra konsumenter till varumärkeskännedom och köpbeslut. Syftet med denna studie är att undersöka hur varumärken använder sig utav influencers som ett marknadsföringsverktyg för att skapa trovärdighet genom samarbeten på sociala medier. Studien har undersökt vad det finns för möjligheter samt problem som denna typ av marknadsföringsverktyg kan innebära. För att undersöka frågeställningen har författarna intervjuat två medelstora influencers och två stora varumärken inom skönhet. Deras expertis och kunskap inom influencer marketing har bidragit till ett exceptionellt forskningsmaterial för denna studie. Slutsatsen visade på att varumärken som använder influencers som ett marknadsföringsverktyg gör det i syfte att stärka relationen till sina kunder, samtidigt som de vill öka sin varumärkeskännedom. Influencers som samarbetar med varumärken anser sig själva inspirera sina följare att prova nya varumärken, samtidigt som de ger en ärlig och rättvis recension. Detta sammanlänkar de tre delarna som ingår i marknadsföringsverktyget, vilket är influencer, mottagare och varumärke. / The fact that companies use influencers as a marketing tool on social media is becoming increasingly more common. Just as the English origin word influence means, influencer marketing is about brands using someone with a large number of followers and a power to affect and influence other consumers to brand awareness and purchasing decisions. The purpose of this study is to investigate how brands use influencers as a marketing tool to create credibility through collaborations on social media. The study will examine what opportunities there are as well as problems that this type of marketing tool can entail. To answer the research question, the authors have interviewed two medium-sized influencers and two major beauty brands. Their expertise and knowledge in influencer marketing have contributed to an exceptional research material for this study. The conclusion showed that brands that use influencers as a marketing tool do it, in order to strengthen the relationship with their customers, while at the same time increase their brand awareness. Influencers who work with brands consider themselves to inspire their followers to try new brands, while giving them an honest and fair review. This conclusion links the three parts included in the marketing tool, which are influencers, recipients and brands.