Effect of orthographic processes on letter identity and letter-position encoding in dyslexic children.

The ability to identify letters and encode their position is a crucial step of the word recognition process. However and despite their word identification problem, the ability of dyslexic children to encode letter identity and letter-position within strings was not systematically investigated. This study aimed at filling this gap and further explored how letter identity and letter-position encoding is modulated by letter context in developmental dyslexia. For this purpose, a letter-string comparison task was administered to French dyslexic children and two chronological age (CA) and reading age (RA)-matched control groups. Children had to judge whether two successively and briefly presented four-letter strings were identical or different. Letter-position and letter identity were manipulated through the transposition (e.g., RTGM vs. RMGT) or substitution of two letters (e.g., TSHF vs. TGHD). Non-words, pseudo-words, and words were used as stimuli to investigate sub-lexical and lexical effects on letter encoding. Dyslexic children showed both substitution and transposition detection problems relative to CA-controls. A substitution advantage over transpositions was only found for words in dyslexic children whereas it extended to pseudo-words in RA-controls and to all type of items in CA-controls. Letters were better identified in the dyslexic group when belonging to orthographically familiar strings. Letter-position encoding was very impaired in dyslexic children who did not show any word context effect in contrast to CA-controls. Overall, the current findings point to a strong letter identity and letter-position encoding disorder in developmental dyslexia.

Désambiguisation de sens par modèles de contextes et son application à la Recherche d’Information

Il est connu que les problèmes d'ambiguïté de la langue ont un effet néfaste sur les résultats des systèmes de Recherche d'Information (RI). Toutefois, les efforts de recherche visant à intégrer des techniques de Désambiguisation de Sens (DS) à la RI n'ont pas porté fruit. La plupart des études sur le sujet obtiennent effectivement des résultats négatifs ou peu convaincants. De plus, des investigations basées sur l'ajout d'ambiguïté artificielle concluent qu'il faudrait une très haute précision de désambiguation pour arriver à un effet positif. Ce mémoire vise à développer de nouvelles approches plus performantes et efficaces, se concentrant sur l'utilisation de statistiques de cooccurrence afin de construire des modèles de contexte. Ces modèles pourront ensuite servir à effectuer une discrimination de sens entre une requête et les documents d'une collection. Dans ce mémoire à deux parties, nous ferons tout d'abord une investigation de la force de la relation entre un mot et les mots présents dans son contexte, proposant une méthode d'apprentissage du poids d'un mot de contexte en fonction de sa distance du mot modélisé dans le document. Cette méthode repose sur l'idée que des modèles de contextes faits à partir d'échantillons aléatoires de mots en contexte devraient être similaires. Des expériences en anglais et en japonais montrent que la force de relation en fonction de la distance suit généralement une loi de puissance négative. Les poids résultant des expériences sont ensuite utilisés dans la construction de systèmes de DS Bayes Naïfs. Des évaluations de ces systèmes sur les données de l'atelier Semeval en anglais pour la tâche Semeval-2007 English Lexical Sample, puis en japonais pour la tâche Semeval-2010 Japanese WSD, montrent que les systèmes ont des résultats comparables à l'état de l'art, bien qu'ils soient bien plus légers, et ne dépendent pas d'outils ou de ressources linguistiques. La deuxième partie de ce mémoire vise à adapter les méthodes développées à des applications de Recherche d'Information. Ces applications ont la difficulté additionnelle de ne pas pouvoir dépendre de données créées manuellement. Nous proposons donc des modèles de contextes à variables latentes basés sur l'Allocation Dirichlet Latente (LDA). Ceux-ci seront combinés à la méthodes de vraisemblance de requête par modèles de langue. En évaluant le système résultant sur trois collections de la conférence TREC (Text REtrieval Conference), nous observons une amélioration proportionnelle moyenne de 12% du MAP et 23% du GMAP. Les gains se font surtout sur les requêtes difficiles, augmentant la stabilité des résultats. Ces expériences seraient la première application positive de techniques de DS sur des tâches de RI standard.

L'atténuation statistique des surdétections d'un correcteur grammatical symbolique

Les logiciels de correction grammaticale commettent parfois des détections illégitimes (fausses alertes), que nous appelons ici surdétections. La présente étude décrit les expériences de mise au point d’un système créé pour identifier et mettre en sourdine les surdétections produites par le correcteur du français conçu par la société Druide informatique. Plusieurs classificateurs ont été entraînés de manière supervisée sur 14 types de détections faites par le correcteur, en employant des traits couvrant di-verses informations linguistiques (dépendances et catégories syntaxiques, exploration du contexte des mots, etc.) extraites de phrases avec et sans surdétections. Huit des 14 classificateurs développés sont maintenant intégrés à la nouvelle version d’un correcteur commercial très populaire. Nos expériences ont aussi montré que les modèles de langue probabilistes, les SVM et la désambiguïsation sémantique améliorent la qualité de ces classificateurs. Ce travail est un exemple réussi de déploiement d’une approche d’apprentissage machine au service d’une application langagière grand public robuste.

A context noise model of episodic word recognition

Item noise models of recognition assert that interference at retrieval is generated by the words from the study list. Context noise models of recognition assert that interference at retrieval is generated by the contexts in which the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate how it can account for data from the item noise and dual-processing approaches to recognition memory. From the item noise perspective, list strength and list length effects, the mirror effect for word frequency and concreteness, and the effects of the similarity of other words in a list are considered. From the dual-processing perspective, process dissociation data on the effects of length, temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise approach to recognition is a viable alternative to existing approaches.

Impact of Word-of-Mouth on consumer decision-making: An information processing perspective in the context of a high-involvement service

This doctoral study conducts an empirical analysis of the impact of Word-of-Mouth (WOM) on marketing-relevant outcomes such as attitudes and consumer choice, during a high-involvement and complex service decision. Due to its importance to decisionmaking, WOM has attracted interest from academia and practitioners for decades. Consumers are known to discuss products and services with one another. These discussions help consumers to form an evaluative opinion, as WOM reduces perceived risk, simplifies complexity, and increases the confidence of consumers in decisionmaking. These discussions are also highly impactful as WOM is a trustworthy source of information, since it is independent from the company or brand. In responding to the calls for more research on what happens after WOM information is received, and how it affects marketing-relevant outcomes, this dissertation extends prior WOM literature by investigating how consumers process information in a highinvolvement service domain, in particular higher-education. Further, the dissertation studies how the form of WOM influences consumer choice. The research contributes to WOM and services marketing literature by developing and empirically testing a framework for information processing and studying the long-term effects of WOM. The results of the dissertation are presented in five research publications. The publications are based on longitudinal data. The research leads to the development of a proposed theoretical framework for the processing of WOM, based on theories from social psychology. The framework is specifically focused on service decisions, as it takes into account evaluation difficulty through the complex nature of choice criteria associated with service purchase decisions. Further, other gaps in current WOM literature are taken into account by, for example, examining how the source of WOM and service values affects the processing mechanism. The research also provides implications for managers aiming to trigger favorable WOM through marketing efforts, such as advertising and testimonials. The results provide suggestions on how to design these marketing efforts by taking into account the mechanism through which information is processed, or the form of social influence.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Good readers use context-sensitive grapheme-phoneme correspondences

Fifth-grade children were given a series of word reading tasks. First, two sets of 16 disyllabic words with medial VCV spellings, and with a long initial vowel were selected, varying in frequency but with similar word-initial segments. Nonwords were derived from these sets of words by exchanging initial onsets. Children read these nonwords in a first testing session. In a second test session, children were given the Woodcock Word Identification Test and the set of analogue words from which the nonwords were derived. Initial analyses examined only nonwords derived from words that were correctly read. Both sets of nonwords were more likely to be read with a long initial vowel than a short initial vowel, although this tendency was stronger in nonwords derived from high frequency words. Furthennore, Word Identification ability showed a strong relationship with the preference for long initial vowels in this type of disyllabic nonword, both for nonwords derived from known analogues and for nonwords derived from words that children could not read correctly. This preference was also correlated with the preference for context-sensitive grapheme-phoneme correspondences in the reading of ambiguous monosyllabic nonwords.These results have strong implications for current theories of word reading.

Grammatical priming of visual word recognition in fourth-grade children

Previous work examining context effects in children has been limited to semantic context. The current research examined the effects of grammatical priming of word-naming in fourth-grade children. In Experiment 1, children named both inflected and uninflected noun and verb target words faster when they were preceded by grammatically constraining primes than when they were preceded by neutral primes. Experiment 1 used a long stimulus onset asynchrony (SOA) interval of 750 msec. Experiment 2 replicated the grammatical priming effect at two SOA intervals (400 msec and 700 msec), suggesting that the grammatical priming effect does not reflect the operation of any gross strategic effects directly attributable to the long SOA interval employed in Experiment 1. Grammatical context appears to facilitate target word naming by constraining target word class. Further work is required to elucidate the loci of this effect.

A Context Noise Model of Episodic Recognition Memory

Item noise models of recognition assert that interference at retrieval is generated by the words from the study list. Context noise models of recognition assert that interference at retrieval is generated by the contexts in which the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate how it can account for data from the item noise and dual-processing approaches to recognition memory. From the item noise perspective, list strength and list length effects, the mirror effect for word frequency and concreteness, and the effects of the similarity of other words in a list are considered. From the dual-processing perspective, process dissociation data on the effects of length. temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise approach to recognition is a viable alternative to existing approaches. (PsycINFO Database Record (c) 2008 APA, all rights reserved)

O declínio e a ascensão da comunicação word-of-mouth

As questões de liderança de opinião tornaram-se um tema cada vez mais interessante na nossa contemporaneidade. Quer se considere, ou não, que os ditos “novos media” provocaram uma revolução na forma como interagimos, o que é inquestionável é o seu papel na forma como somos influenciados e influenciamos, pelo menos, no que às sociedades ditas como desenvolvidas se refere. Partindo do interesse pelo estudo do word-of-mouth eletrónico, esta dissertação pretende contribuir para um melhor conhecimento do Twitter enquanto instrumento de influência. De um ponto de vista empírico este trabalho centrou-se na análise do Twitter de Arianna Huffington e dos seus seguidores no Twitter.

Does Semantic Context Benefit Speech Understanding through "Top-Down" Processes? Evidence from Time-resolved Sparse fMRI.

When speech is degraded, word report is higher for semantically coherent sentences (e.g., her new skirt was made of denim) than for anomalous sentences (e.g., her good slope was done in carrot). Such increased intelligibility is often described as resulting from "top-down" processes, reflecting an assumption that higher-level (semantic) neural processes support lower-level (perceptual) mechanisms. We used time-resolved sparse fMRI to test for top-down neural mechanisms, measuring activity while participants heard coherent and anomalous sentences presented in speech envelope/spectrum noise at varying signal-to-noise ratios (SNR). The timing of BOLD responses to more intelligible speech provides evidence of hierarchical organization, with earlier responses in peri-auditory regions of the posterior superior temporal gyrus than in more distant temporal and frontal regions. Despite Sentence content × SNR interactions in the superior temporal gyrus, prefrontal regions respond after auditory/perceptual regions. Although we cannot rule out top-down effects, this pattern is more compatible with a purely feedforward or bottom-up account, in which the results of lower-level perceptual processing are passed to inferior frontal regions. Behavioral and neural evidence that sentence content influences perception of degraded speech does not necessarily imply "top-down" neural processes.

Lexical Influences on Spoken Spondaic Word Recognition in Hearing-Impaired Patients.

Top-down contextual influences play a major part in speech understanding, especially in hearing-impaired patients with deteriorated auditory input. Those influences are most obvious in difficult listening situations, such as listening to sentences in noise but can also be observed at the word level under more favorable conditions, as in one of the most commonly used tasks in audiology, i.e., repeating isolated words in silence. This study aimed to explore the role of top-down contextual influences and their dependence on lexical factors and patient-specific factors using standard clinical linguistic material. Spondaic word perception was tested in 160 hearing-impaired patients aged 23-88 years with a four-frequency average pure-tone threshold ranging from 21 to 88 dB HL. Sixty spondaic words were randomly presented at a level adjusted to correspond to a speech perception score ranging between 40 and 70% of the performance intensity function obtained using monosyllabic words. Phoneme and whole-word recognition scores were used to calculate two context-influence indices (the j factor and the ratio of word scores to phonemic scores) and were correlated with linguistic factors, such as the phonological neighborhood density and several indices of word occurrence frequencies. Contextual influence was greater for spondaic words than in similar studies using monosyllabic words, with an overall j factor of 2.07 (SD = 0.5). For both indices, context use decreased with increasing hearing loss once the average hearing loss exceeded 55 dB HL. In right-handed patients, significantly greater context influence was observed for words presented in the right ears than for words presented in the left, especially in patients with many years of education. The correlations between raw word scores (and context influence indices) and word occurrence frequencies showed a significant age-dependent effect, with a stronger correlation between perception scores and word occurrence frequencies when the occurrence frequencies were based on the years corresponding to the patients' youth, showing a "historic" word frequency effect. This effect was still observed for patients with few years of formal education, but recent occurrence frequencies based on current word exposure had a stronger influence for those patients, especially for younger ones.

Functional neuroanatomy of meaning acquisition from context

An important issue in language learning is how new words are integrated in the brain representations that sustain language processing. To identify the brain regions involved in meaning acquisition and word learning, we conducted a functional magnetic resonance imaging study. Young participants were required to deduce the meaning of a novel word presented within increasingly constrained sentence contexts that were read silently during the scanning session. Inconsistent contexts were also presented in which no meaning could be assigned to the novel word. Participants showed meaning acquisition in the consistent but not in the inconsistent condition. A distributed brain network was identified comprising the left anterior inferior frontal gyrus (BA 45), the middle temporal gyrus (BA 21), the parahippocampal gyrus, and several subcortical structures (the thalamus and the striatum). Drawing on previous neuroimaging evidence, we tentatively identify the roles of these brain areas in the retrieval, selection, and encoding of the meaning.