"Talent, Skill and Support." : A Method for Automatic Creation of Slogans

Slogans are an effective way to convey a marketing message. In this paper, we present a method for automatically creating slogans, aimed to facilitate a human slogan designer in her creative process. By taking a target concept (e.g. a computer) and an adjectival property (e.g. creative) as input, the proposed method produces a list of diverse expressions optimizing multiple objectives such as semantic relatedness, language correctness, and usage of rhetorical devices. A key component in the process is a novel method for generating nominal metaphors based on a metaphor interpretation model. Using the generated metaphors, the method builds semantic spaces related to the objectives. It extracts skeletons from existing slogans, and finally fills them in, traversing the semantic spaces, using the genetic algorithm to reach interesting solutions (e.g. “Talent, Skill and Support.”). We evaluate both the metaphor generation method and the overall slogan creation method by running two crowdsourced questionnaires.Peer reviewe

Computational Analysis and Generation of Slogans

I reklam används sloganer för att förbättra återkallandet av den annonserade produkten av konsumenter och skilja den från andra på marknaden. Att skapa effektiva slagord är en resurskrävande uppgift för människor. I denna avhandling beskriver vi en ny metod för att automatiskt generera sloganer, med tanke på ett målkoncept (t ex bil) och en adjektivsegenskap för att uttrycka (t ex elegant) som input. Dessutom föreslår vi en metod för att generera nominella metaforer med hjälp av en metafor-tolkningsmodell för att möjliggöra generering av metaforiska slagord. Metoden för att generera sloganer extraherar skelett från befintliga sloganer, så fyller det ett skelett med lämpliga ord genom att använda flera språkliga resurser (som ett förvar av grammatiska och semantiska relationer och språkmodeller) och genetiska algoritmer samtidigt som man optimerar flera mål såsom semantiska relateradhet, språkkorrigering och användning av retoriska enheter. Vi utvärderar metaforen och slogangenereringsmetoderna med hjälp av en tänktalkoplattform. På en 5-punkts Likert-skala ber vi online-domare att bedöma de genererade metaforerna tillsammans med tre andra metaforer som genererades med andra metoder och visa hur bra de kommunicerar den eftersökta betydelsen. Slogangenereringsmetoden utvärderas genom att be crowdsourced-domare att bedöma genererade sloganer från fem perspektiv, vilka är 1) hur bra är sloganet relaterat till ämnet, 2) hur korrekt är sloganets språk, 3) hur metaforiskt är sloganet, 4) hur engagerande, attraktivt och minnesvärt är det och 5) hur bra är sloganet överlag. Dessa frågor är utvalda för att undersöka effekterna av relateradhet till produkten och den markerade egenskapen, användningen av retoriska anordningar och språkets korrekthet på den övergripande uppskattningen av slogan. På samma sätt utvärderar vi befintliga sloganer som har skapats av äkta människor. Baserat på utvärderingarna analyserar vi metoden som helhet tillsammans med de enskilda optimeringsfunktionerna och ger insikter om befintliga sloganer. Resultaten från våra utvärderingar visar att vår metaforgeneringsmetod kan producera lämpliga metaforer. För slogangenereraren bevisar resultaten att metoden har varit framgångsrik i att producera minst en effektiv slogan för varje utvärderad input. Ändå finns det utrymme för att förbättra metoden, som diskuteras i slutet av avhandlingen

Semantic Processing of Nominal Metaphor: Figurative Abstraction and Embodied Simulation

In a metaphor such as that lawyer is a shark, the concept lawyer, which is the metaphor topic, and the concept shark, which is the metaphor vehicle, interact to produce a figurative meaning such that lawyers are predatory. Some theorists argue that sensorimotor properties of the vehicle are the basis of metaphor comprehension (Gibbs & Matlock, 2008; Paivio, 1979; Wilson & Gibbs, 2007). As such, that lawyer is a shark is processed by an embodied simulation where sensorimotor imagery associated with sharks is simulated (e.g., sharks hunting in deep water). However, the long-standing assumption is that metaphors are processed abstractly and sensorimotor representations play no role (e.g., Gentner & Bowdle, 2008; Glucksberg, 2008). This thesis examines the role of sensorimotor simulation in processing metaphor. In Studies 1 – 2, participants rated metaphors on comprehensibility. The metaphors contained vehicles that varied on a semantic richness variable known as body-object interaction (BOI), which characterizes the degree to which a concept is easy to interact with (Siakaluk et al., 2008). A high-BOI metaphor contains a vehicle concept that is easy-to-interact with (e.g., life is a bicycle) whereas a low-BOI metaphor contains a concept that is difficult-to-interact with (e.g., life is a rainbow). Participants rated low-BOI metaphors to be more comprehensible than high-BOI metaphors, a finding that suggests sensorimotor properties are not heavily involved in metaphor processing. In Study 3 participants created novel metaphors by pairing abstract topics with words that varied on BOI to serve as vehicles. In creating metaphors, participants chose more low-BOI words to serve as vehicles than high-BOI words. However, to interpret their created metaphors, participants used language reflective of an embodied simulation for both high and low-BOI metaphors, indicating that nominal metaphors do indeed involve sensorimotor imagery. In Studies 4 – 7, a priming paradigm showed that processing novel metaphors (e.g., highways are snakes) immediately activates sensorimotor properties (e.g., slither) whereas familiar metaphors (e.g., lawyers are sharks) do not activate sensorimotor properties (e.g., bite) but rather, activate abstract associations (e.g., killer). In sum, the experiments in this dissertation are the first to demonstrate novel metaphors are processed by sensorimotor simulations

Sentiment Analysis for Words and Fiction Characters From The Perspective of Computational (Neuro-)Poetics

Two computational studies provide different sentiment analyses for text segments (e.g., ‘fearful’ passages) and figures (e.g., ‘Voldemort’) from the Harry Potter books (Rowling, 1997 - 2007) based on a novel simple tool called SentiArt. The tool uses vector space models together with theory-guided, empirically validated label lists to compute the valence of each word in a text by locating its position in a 2d emotion potential space spanned by the > 2 million words of the vector space model. After testing the tool’s accuracy with empirical data from a neurocognitive study, it was applied to compute emotional figure profiles and personality figure profiles (inspired by the so-called ‚big five’ personality theory) for main characters from the book series. The results of comparative analyses using different machine-learning classifiers (e.g., AdaBoost, Neural Net) show that SentiArt performs very well in predicting the emotion potential of text passages. It also produces plausible predictions regarding the emotional and personality profile of fiction characters which are correctly identified on the basis of eight character features, and it achieves a good cross-validation accuracy in classifying 100 figures into ‘good’ vs. ‘bad’ ones. The results are discussed with regard to potential applications of SentiArt in digital literary, applied reading and neurocognitive poetics studies such as the quantification of the hybrid hero potential of figures

The Cognitive Psychology of Humour in Written Puns

The primary purpose of this dissertation was to investigate how humour from written puns is produced. Prior models have emphasized that novel or surprising incongruities should be important to humour appreciation­ (Suls, 1972; Topolinski, 2014). In study 1, a new approach to operationalizing incongruity as semantic dissimilarity was developed and tested using Latent Semantic Analysis (Landauer, Foltz & Laham, 1998). “Latent semantic incongruity” was associated with humour ratings, but only for puns with low ratings of familiarity from a prior occasion or for those with a low level of aggressive content. Overall, there was also an unexpected strong positive association between familiarity with a pun from a prior occasion and humour ratings. Study 2 demonstrated that humour ratings for puns decreases with repeated exposures. Changes in humour with repetition were dependent on latent semantic incongruity, the duration of time spent comprehending the pun and providing humour ratings, and on how humour was measured. Study 3 investigated whether “elaboration” on the two implied concepts in each pun was associated with humour (as predicted by Wyer & Collins, 1992). Elaboration quantity (the number of associated words that participants could comfortably list) and elaboration duration (the duration of time participants spent on the elaboration task) were associated with humour ratings, but only for familiar puns. In summary, fluent comprehension of incongruity was important to humour from unfamiliar puns, whereas elaboration on the implied concepts in puns was important to humour appreciation for puns that were familiar from a prior occasion

A distributional model of semantic context effects in lexical processinga

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›

Economic Complexity Unfolded: Interpretable Model for the Productive Structure of Economies

Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. It resides on the premise of hidden capabilities - fundamental endowments underlying the productive structure. In general, measuring the capabilities behind economic complexity directly is difficult, and indirect measures have been suggested which exploit the fact that the presence of the capabilities is expressed in a country's mix of products. We complement these studies by introducing a probabilistic framework which leverages Bayesian non-parametric techniques to extract the dominant features behind the comparative advantage in exported products. Based on economic evidence and trade data, we place a restricted Indian Buffet Process on the distribution of countries' capability endowment, appealing to a culinary metaphor to model the process of capability acquisition. The approach comes with a unique level of interpretability, as it produces a concise and economically plausible description of the instantiated capabilities

The roles of familiarity and context in processing Chinese xiehouyu : an ERP study

This study conducts an ERP experiment to explore the online processing mechanism of Chinese xiehouyu, a subcategory of Chinese idiomatic expressions with a metaphorical two-part allegorical saying, regarded as a non-literal language construct. Using a 2 × 2 design, (high familiarity (HF)/low familiarity (LF)) × (literally-biasing context (LC)/metaphorically-biasing context (MC)), the researchers have obtained the following findings: (1) familiarity plays an important role in Chinese xiehouyu processing, i.e. the metaphorical meaning of a HF Chinese xiehouyu can be directly activated while that of a LF one has to be derived from its literal meaning first; (2) contextual information also weighs in the process, i.e. the metaphorical meaning of a Chinese xiehouyu can be promoted in MC condition but suppressed in LC condition; (3) the interactive effect of familiarity and contextual information can be explained by the career of metaphor hypothesis; and (4) the Standard Pragmatic Model (SPM) of non-literal languages can explain the processing of LF xiehouyu, and the Direct Access Model (DAM) may to some extent account for the mechanism of HF one but fails to explain the case of LF one, while the Graded Salience Hypothesis (GSH) can provide an acceptable explanation for the processing mechanism of Chinese xiehouyus of varied familiarity