Лингвистическая игра слов в повестях Льюиса Кэрролла «Приключения Алисы в стране чудес» и «Сквозь зеркало, и что там нашла Алиса»

Рассмотрены отдельные примеры игры слов в сказках Льюиса Кэрролла. Проанализировано понятие «игра слов» и его типология, а также представлены способы употребления этого стилистического приема в сказочных повестях Льюиса Кэрролла

A linguistic wordplay in Lewis Carroll's story "Alice In Wonderland"

In this article some wordplay examples in Lewis Carroll's story ЂAlice in Wonderlandї are considered. The concept of Ђwordplayї is analyzed, some ways of using wordplay in the story are shown, and their impact on the story itself and on the scholarsТ interest is emphasized

A linguistic wordplay in Lewis Carroll's story "Alice In Wonderland"

In this article some wordplay examples in Lewis Carroll's story ЂAlice in Wonderlandї are considered. The concept of Ђwordplayї is analyzed, some ways of using wordplay in the story are shown, and their impact on the story itself and on the scholarsТ interest is emphasized

Natural Language Processing for Foreign Language Learning

This research presents novel algorithms which generate sentences in a natural language, using natural language generation techniques. The purpose of the algorithms is to benefit foreign language learning. As far as we can tell, ours is the first such research being done in the field. In creating the algorithms, we also developed a piece of software to showcase the work and allow testing by users. The main algorithm begins by generating sentence models by using one of two methods, namely modeled sentence generation and semantic sentence generation. Each of these have benefits and drawbacks, which the user must take into consideration when generating sentences. When the models are generated, they are filled in word by word using a conjugation algorithm. The completed sentences are then returned to the user and may then be translated. There is still much work to do before we will be satisfied with the algorithms, but our research shows that it is possible to use natural language generation techniques to benefit foreign language learning

Computational Understanding, Generation and Evaluation of Creative Expressions

Computational creativity has received a good amount of research interest in generating creative artefacts programmatically. At the same time, research has been conducted in computational aesthetics, which essentially tries to analyse creativity exhibited in art. This thesis aims to unite these two distinct lines of research in the context of natural language generation by building, from models for interpretation and generation, a cohesive whole that can assess its own generations. I present a novel method for interpreting one of the most difficult rhetoric devices in the figurative use of language: metaphors. The method does not rely on hand-annotated data and it is purely data-driven. It obtains the state of the art results and is comparable to the interpretations given by humans. We show how a metaphor interpretation model can be used in generating metaphors and metaphorical expressions. Furthermore, as a creative natural language generation task, we demonstrate assigning creative names to colours using an algorithmic approach that leverages a knowledge base of stereotypical associations for colours. Colour names produced by the approach were favoured by human judges to names given by humans 70% of the time. A genetic algorithm-based method is elaborated for slogan generation. The use of a genetic algorithm makes it possible to model the generation of text while optimising multiple fitness functions, as part of the evolutionary process, to assess the aesthetic quality of the output. Our evaluation indicates that having multiple balanced aesthetics outperforms a single maximised aesthetic. From an interplay of neural networks and the traditional AI approach of genetic algorithms, we present a symbiotic framework. This is called the master-apprentice framework. This makes it possible for the system to produce more diverse output as the neural network can learn from both the genetic algorithm and real people. The master-apprentice framework emphasises a strong theoretical foundation for the creative problem one seeks to solve. From this theoretical foundation, a reasoned evaluation method can be derived. This thesis presents two different evaluation practices based on two different theories on computational creativity. This research is conducted in two distinct practical tasks: pun generation in English and poetry generation in Finnish.Laskennallista luovuutta on tutkittu paljon puhtaan tuottamisen näkökulmasta ja saman aikaan tutkimusta on tehty laskennallisen estetiikan saralla. Väitöskirjani yhdistää näitä kahta eri koulukuntaa, sillä kehittämäni laskennallisesti luovat järjestelmät käyttävät tuottamisessa apuna estetiikkaa; järjestelmät siis tulkitsevat teoksiaan samaan aikaan, kun ne niitä tuottavat. Käsittelen väitöskirjassani metaforien automaattista tulkintaa, värien nimien tuottamista, sloganien tuottamista sekä suomenkielisen runouden tuottamista. Metodeina käytän perinteistä koneoppimisalgoritmia, eli niin kutsuttua geneettistä algoritmia, sekä neuroverkkoja. Niiden yhdistelmää nimitän mestari ja oppipoika -malliksi, jossa geneettinen algoritmi opettaa neuroverkkoja

Sobre la computación del humor. Del uso de estereotipos para la computación de construcciones potencialmente humorísticas

Desconocido para muchos, el campo de estudios denominado humor computacional se encuentra en un estado incipiente. En este trabajo, presentamos el estado del arte en esta materia, repasando las teorías generales y lingüísticas del humor más reconocidas, para luego entrar concretamente en el campo de la computación, donde ya se han desarrollado las primeras aplicaciones de generación y reconocimiento automático de humor. Dado este marco, nos adentramos en un área poco explorada: la computación del humor basado en lo que dimos por llamar conocimiento informal, en particular, estereotipos. Proponemos un método para computar una clase particular de estereotipos, analizando de forma automática su potencial humorístico

Adjusting Sense Representations for Word Sense Disambiguation and Automatic Pun Interpretation

Word sense disambiguation (WSD)—the task of determining which meaning a word carries in a particular context—is a core research problem in computational linguistics. Though it has long been recognized that supervised (machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs). These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. This dissertation is concerned with extending the efficacy and applicability of knowledge-based word sense disambiguation. First, we investigate two approaches for bridging the information gap and thereby improving the performance of knowledge-based WSD. In the first approach we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. Our next main contribution is to adapt techniques from word sense disambiguation to a novel task: the interpretation of puns. Traditional NLP applications, including WSD, usually treat the source text as carrying a single meaning, and therefore cannot cope with the intentionally ambiguous constructions found in humour and wordplay. We describe how algorithms and evaluation methodologies from traditional word sense disambiguation can be adapted for the "disambiguation" of puns, or rather for the identification of their double meanings. Finally, we cover the design and construction of technological and linguistic resources aimed at supporting the research and application of word sense disambiguation. Development and comparison of WSD systems has long been hampered by a lack of standardized data formats, language resources, software components, and workflows. To address this issue, we designed and implemented a modular, extensible framework for WSD. It implements, encapsulates, and aggregates reusable, interoperable components using UIMA, an industry-standard information processing architecture. We have also produced two large sense-annotated data sets for under-resourced languages or domains: one of these targets German-language text, and the other English-language puns