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Os documentos oficiais e a concretização das propostas no livro didático analisado abordam a ambiguidade lexical como algo a ser evitado e corrigido através de estratégias de desambiguação, ignorando a importância da natureza polissêmica da língua como recurso expressivo na comunicação cotidiana e como instrumento fundamental do humor, especialmente as variantes de humor popular e infantil. O presente trabalho busca abordar a ambiguidade lexical, polissemia e homonímia, no gênero tirinha cômica, texto multimodal e humorístico, visando observar a eficácia da proposta de leitura e produção como exercício de contextualização, levando os alunos a observarem, explicarem e produzirem texto, utilizando-se dos diversos significados possíveis das palavras em uso, em situação concreta de enunciação. A proposta se dá dentro do conceito de multimodalidade, trabalhando a ambiguidade lexical e o letramento visual, objetivando que o aluno não apenas observe e compreenda as tirinhas apresentadas, mas também que produza a sua própria, compondo o texto com os elementos verbal e visual. As atividades foram divididas em dez passos: pesquisa e compartilhamento de HQs, atividade diagnóstica, análise da ambiguidade lexical em tirinha cômica no livro didático, jogo digital Quem ri seus males espanta Piadas, consulta às diferentes acepções no dicionário, escrita do glossário de unidades lexicais polissêmicas ou homônimas, elaboração do roteiro da tirinha, autoavaliação, produção final de uma tirinha cômica e apreciação dos trabalhos das turmas. / The analised official papers and textbook proposals approach lexical ambiguity as something to be avoided and correct through desambiguation strategies, ignoring the importance of the polissemic nature of language as an expressive resource of the language in daily communication and as a fundamental tool of comedy and humor, especially popular and childrens jokes and puns. This paper aims to approach lexical ambiguity, polissemy and homonyms, within the comic strip genre, which is a multimodal and comic text genre, with the objetive of observing how effective this proposal of reading and writing can be to help students comprehend, learn and explain lexical ambiguity, by using a word in multiple possible meanings in the comic strip, in concrete enunciation. This proposal uses multimodality, by combining lexical ambiguity and visual litteracy, aiming to enable the student not only to observe and understand the comic strips presented, but also to produce their own by using verbal and visual components. The sequence is divided in ten steps: research and sharing comics, diagnostic evaluation, lexical ambiguity in the textbook, digital game Quem ri seus males espanta Piadas, searching for different meanings of words in the dictionary, writing a glossary of polissemic unities, production of a scratch comic, self evaluation, final production and appreciation of the classes works.

This thesis describes a novel methodology, grounded in the distributional semantic paradigm, for building context sensitive models of word meaning, affording an empirical exploration of the relationship between words and concepts. Anchored in theoretical linguistic insight regarding the contextually specified nature of lexical semantics, the work presented here explores a range of techniques for the selection of subspaces of word co-occurrence dimensions based on a statistical analysis of input terms as observed within large-scale textual corpora. The relationships between word-vectors that emerge in the projected subspaces can be analysed in terms of a mapping between their geometric features and their semantic properties. The power of this modelling technique is its ability to generate ad hoc semantic relationships in response to an extemporaneous linguistic or conceptual situation. The product of this approach is a generalisable computational linguistic methodology, capable of taking input in various forms, including word groupings and sentential context, and dynamically generating output from a broad base model of word co-occurrence data. To demonstrate the versatility of the method, this thesis will present competitive empirical results on a range of established natural language tasks including word similarity and relatedness rating, metaphor and metonymy detection, and analogy completion. A range of techniques will be applied in order to explore the ways in which different aspects of projected geometries can be mapped to different semantic relationships, allowing for the discovery of a range of lexical and conceptual properties for any given input and providing a basis for an empirical exploration of distinctions between the semantic phenomena under analysis. The case made here is that the flexibility of these models and their ability to extend output to evaluations of unattested linguistic relationships constitutes the groundwork for a method for the extrapolation of dynamic conceptual relationships from large-scale textual corpora. This method is presented as a complement and a counterpoint to established distributional methods for generating lexically productive word-vectors. Where contemporary vector space models of distributional semantics have almost universally involved either the factorisation of co-occurrence matrices or the incremental learning of abstract representations using neural networks, the approach described in this thesis preserves the connection between the individual dimensions of word-vectors and statistics pertaining to observations in a textual corpus. The hypothesis tested here is that the maintenance of actual, interpretable information about underlying linguistic data allows for the contextual selection of non-normalised subspaces with more nuanced geometric features. In addition to presenting competitive results for various computational linguistic targets, the thesis will suggest that the transparency of its representations indicates scope for the application of this model to various real-world problems where an interpretable relationship between data and output is highly desirable. This, finally, demonstrates a way towards the productive application of the theory and philosophy of language to computational linguistic practice.

La communication en général, et les capacités linguistiques en particulier, constituent de formidables obstacles pour beaucoup d'enfants avec TSA, qui ont du mal à faire connaître leurs idées, sentiments et intentions à autrui. La sémantique lexicale est fondamentale dans le choix des bons mots et la compréhension du monde autour de soi. Dans la recherche sur le Trouble du spectre de l’autisme (TSA), les capacités langagières sont communément mesurées sur la base de la performance sur des tâches qui mesurent les capacités en sémantique lexicale—les connaissances sur la signification des mots. Or, les connaissances scientifiques sur comment les capacités sémantiques lexicales sont liées aux autres aspects du langage dans le TSA sont pauvres. / Communications in general and linguistic abilities in particular, constitute formidable obstacles for many children with ASD, who struggle with making their ideas, feelings and intentions known to others. Lexical semantics is fundamental to choosing the right words, and understanding the surrounding world. In research on ASD, language abilities are very widely measured on the basis of performance on tasks testing lexical semantic abilities—knowledge of the meaning of words. However, very little is known about how lexical semantic abilities are related to other aspects of language in ASD.

The present study explores the concepts behind Japanese and Swedish dimensional adjectives. The focus is on examining which similarities and differences in the conceptualisation across the two languages exist, if any at all. In order to see how concepts underlying dimensional adjectives are represented in the speaker's minds, data was collected mainly from two word-association tests. The results show that dimensional adjectives are conceptualised and represented differently by speakers of these two languages. The most remarkable difference resulting from the word-association tests is that Japanese participants associate dimensional adjectives mostly with nouns that denote entities the prominent extension of which is aptly described by the stimulus dimensional adjective (e.g. 'long' is associated with 'river'). In Swedish, however, participants associate dimensional adjectives with both adjectives and nouns, and the association patterns and their underlying conceptualisations are thus more diverse (e.g. 'high' is associated with 'building', and 'long' is associated with 'narrow').

In the Bantu language Kinyarwanda, the morpheme –ish can be used to mark both causation and the instrumental applicative. This report pro- poses an explanation for this causative-instrumental syncretism, arguing that both causation and the introduction of an instrument are—at their core—two outgrowths of the same semantic notion. Fitting with other morphological causatives in Bantu, the causative use of –ish patterns as a lexical causative marker. The analysis presented here captures the lex- ical nature of the causative use of the morpheme by arguing that the new causal link is added sub-lexically, situating Kinyarwanda into a cross- linguistic typology of morphological causatives. / text

Orientador: Bento Carlos Dias da Silva / Banca: Clotilde de Almeida Azevedo Murakawa / Banca: Patrícia Tosqui Lucks / Resumo: A cidade de Ibitinga destaca-se nacionalmente no ramo dos bordados, com sua economia voltada quase que exclusivamente à produção dessas peças. Tendo esse contexto como pano de fundo, esta dissertação discute todo o processo linguístico e linguístico-computacional de construção de ontoléxicos - constructos formais cuja natureza léxico-conceitual possibilita o desenvolvimento de investigações teóricas (estudos lexicogramaticais) e aplicadas (construção de recursos lexicográficos e para o processamento computacional de informação textual disponível na Web). Em particular, constrói um ontoléxico exploratório que entrelaça conceitos e itens lexicais do domínio léxico-conceitual da Indústria do Bordado de Ibitinga. O embasamento teórico-metodológico assenta-se nos estudos de semântica lexical (wordnets) e de frames (framenets), pura e computacional, de processamento automático de língua natural e de ontologias. / Abstract: Ibitinga, which is a Brazilian town nationally known for its outstanding position in the embroidery business, has its economy almost totally based on the production of a broad selection of embroideries. With this embroidery industry in the backdrop, this master thesis describes the whole linguistic and computational-linguistic process of construction of ontolexicons - formal constructs whose lexical-conceptual nature allows for both theoretical (lexical-grammar construction) and applied (lexicographical and ontolexical resource construction) research. In particular, the study focuses on the design and implementation of a toy ontolexicon for the Ibitinga Embroydery Industry lexical-conceptual domain. Its theoretical foundations have drawn heavily on lexical semantics (wordnets), frame semantics (framenets), natural language processing, and ontologies. / Mestre

Os documentos oficiais e a concretização das propostas no livro didático analisado abordam a ambiguidade lexical como algo a ser evitado e corrigido através de estratégias de desambiguação, ignorando a importância da natureza polissêmica da língua como recurso expressivo na comunicação cotidiana e como instrumento fundamental do humor, especialmente as variantes de humor popular e infantil. O presente trabalho busca abordar a ambiguidade lexical, polissemia e homonímia, no gênero tirinha cômica, texto multimodal e humorístico, visando observar a eficácia da proposta de leitura e produção como exercício de contextualização, levando os alunos a observarem, explicarem e produzirem texto, utilizando-se dos diversos significados possíveis das palavras em uso, em situação concreta de enunciação. A proposta se dá dentro do conceito de multimodalidade, trabalhando a ambiguidade lexical e o letramento visual, objetivando que o aluno não apenas observe e compreenda as tirinhas apresentadas, mas também que produza a sua própria, compondo o texto com os elementos verbal e visual. As atividades foram divididas em dez passos: pesquisa e compartilhamento de HQs, atividade diagnóstica, análise da ambiguidade lexical em tirinha cômica no livro didático, jogo digital Quem ri seus males espanta Piadas, consulta às diferentes acepções no dicionário, escrita do glossário de unidades lexicais polissêmicas ou homônimas, elaboração do roteiro da tirinha, autoavaliação, produção final de uma tirinha cômica e apreciação dos trabalhos das turmas. / The analised official papers and textbook proposals approach lexical ambiguity as something to be avoided and correct through desambiguation strategies, ignoring the importance of the polissemic nature of language as an expressive resource of the language in daily communication and as a fundamental tool of comedy and humor, especially popular and childrens jokes and puns. This paper aims to approach lexical ambiguity, polissemy and homonyms, within the comic strip genre, which is a multimodal and comic text genre, with the objetive of observing how effective this proposal of reading and writing can be to help students comprehend, learn and explain lexical ambiguity, by using a word in multiple possible meanings in the comic strip, in concrete enunciation. This proposal uses multimodality, by combining lexical ambiguity and visual litteracy, aiming to enable the student not only to observe and understand the comic strips presented, but also to produce their own by using verbal and visual components. The sequence is divided in ten steps: research and sharing comics, diagnostic evaluation, lexical ambiguity in the textbook, digital game Quem ri seus males espanta Piadas, searching for different meanings of words in the dictionary, writing a glossary of polissemic unities, production of a scratch comic, self evaluation, final production and appreciation of the classes works.

The question of how language evolved has received an increasing amount of attention in recent years. Compared to seemingly more complex phenomena such as syntax, word meanings are usually seen as relatively easy to explain. Mainstream accounts in psycholinguistics and evolutionary linguistics assume that word meanings correspond to stable concepts which are prior to language and derive straightforwardly from human perception of structure in the world. Taking a cognitive linguistic approach based on psycholinguistic evidence, I argue instead that word meanings are conventions, grounded, learned and used in the context of communication. The meaning of a word is the sum of its contexts of use, with particular features of these contexts made more or less salient by mechanisms of attentional learning and communicative inference. Evolutionarily, word meanings arise as an emergent product of humans’ adapted tendency to infer each other’s intentions using contextual cues. They are then shaped over cultural evolution by the need to be learnable and useful for communication. This thesis presents a series of experiments that test the effect of these pressures on the origins and development of word meanings. Experiment 1 investigates the origins of strong tendencies for words to specify features on particular dimensions (such as the shape bias). The results show that these tendencies arise via attentional learning effects amplified by iterated learning. Dimensions which are less salient in contexts of learning and use drop out of word meanings as they are passed down a chain of learners. Experiments 2, 3 and 4 investigate the structure of word meanings produced during either paired communication games or individual labelling of images by similarity. While communication alone leads to word meanings that are unstructured and poorly aligned within pairs, communication plus iterated learning leads to word meanings that increase in structure and alignment over generations. Finally, Experiment 5 investigates the interaction of event structure and developing conventions in shaping word meanings. The structure of events in an artificial world is shown to influence lexicalisation patterns in the languages conventionalised by communicating pairs. Event features that are less predictable across communicative contexts tend to be more strongly associated with the conventions in the language. Overall, the experiments show that rather than straightforwardly reflecting pre-linguistic conceptualisation, word meanings are also dynamically shaped by learning and communication. In addition, these processes are constrained by the conventions that already exist within a language. This illuminates the mixture of convergence and diversity we see in word meanings in natural languages, and gives insight into their evolutionary origins.

Humans possess a rich semantic knowledge of words and concepts which captures the perceivable physical properties of their real-world referents and their relations. Encoding this knowledge or some of its aspects is the goal of computational models of semantic representation and has been the subject of considerable research in cognitive science, natural language processing, and related areas. Existing models have placed emphasis on different aspects of meaning, depending ultimately on the task at hand. Typically, such models have been used in tasks addressing the simulation of behavioural phenomena, e.g., lexical priming or categorisation, as well as in natural language applications, such as information retrieval, document classification, or semantic role labelling. A major strand of research popular across disciplines focuses on models which induce semantic representations from text corpora. These models are based on the hypothesis that the meaning of words is established by their distributional relation to other words (Harris, 1954). Despite their widespread use, distributional models of word meaning have been criticised as ‘disembodied’ in that they are not grounded in perception and action (Perfetti, 1998; Barsalou, 1999; Glenberg and Kaschak, 2002). This lack of grounding contrasts with many experimental studies suggesting that meaning is acquired not only from exposure to the linguistic environment but also from our interaction with the physical world (Landau et al., 1998; Bornstein et al., 2004). This criticism has led to the emergence of new models aiming at inducing perceptually grounded semantic representations. Essentially, existing approaches learn meaning representations from multiple views corresponding to different modalities, i.e. linguistic and perceptual input. To approximate the perceptual modality, previous work has relied largely on semantic attributes collected from humans (e.g., is round, is sour), or on automatically extracted image features. Semantic attributes have a long-standing tradition in cognitive science and are thought to represent salient psychological aspects of word meaning including multisensory information. However, their elicitation from human subjects limits the scope of computational models to a small number of concepts for which attributes are available. In this thesis, we present an approach which draws inspiration from the successful application of attribute classifiers in image classification, and represent images and the concepts depicted by them by automatically predicted visual attributes. To this end, we create a dataset comprising nearly 700K images and a taxonomy of 636 visual attributes and use it to train attribute classifiers. We show that their predictions can act as a substitute for human-produced attributes without any critical information loss. In line with the attribute-based approximation of the visual modality, we represent the linguistic modality by textual attributes which we obtain with an off-the-shelf distributional model. Having first established this core contribution of a novel modelling framework for grounded meaning representations based on semantic attributes, we show that these can be integrated into existing approaches to perceptually grounded representations. We then introduce a model which is formulated as a stacked autoencoder (a variant of multilayer neural networks), which learns higher-level meaning representations by mapping words and images, represented by attributes, into a common embedding space. In contrast to most previous approaches to multimodal learning using different variants of deep networks and data sources, our model is defined at a finer level of granularity—it computes representations for individual words and is unique in its use of attributes as a means of representing the textual and visual modalities. We evaluate the effectiveness of the representations learnt by our model by assessing its ability to account for human behaviour on three semantic tasks, namely word similarity, concept categorisation, and typicality of category members. With respect to the word similarity task, we focus on the model’s ability to capture similarity in both the meaning and appearance of the words’ referents. Since existing benchmark datasets on word similarity do not distinguish between these two dimensions and often contain abstract words, we create a new dataset in a large-scale experiment where participants are asked to give two ratings per word pair expressing their semantic and visual similarity, respectively. Experimental results show that our model learns meaningful representations which are more accurate than models based on individual modalities or different modality integration mechanisms. The presented model is furthermore able to predict textual attributes for new concepts given their visual attribute predictions only, which we demonstrate by comparing model output with human generated attributes. Finally, we show the model’s effectiveness in an image-based task on visual category learning, in which images are used as a stand-in for real-world objects.