La acción performativa en la liturgia medieval : ¿gesto o palabra?

Este trabajo analiza las características del gesto ritual propio de la liturgia medieval, en particular su carácter de “eficaz". La gestualidad litúrgica “hace cosas", al decir de John Austin; en tanto produce cambios en sus destinatarios que llevan, incluso, a provocar modificaciones permanentes en sus vidas. ¿En qué consiste esa eficacia? ¿La eficacia la posee la palabra o el gesto litúrgico? La hipótesis que se plantea es que, si bien en el discurso académico la performatividad proviene de la palabra, no sucede lo mismo en el discurso popular puesto que, en este caso, la eficacia proviene del gesto. El trabajo se estructura en cuatro partes. Luego de una introducción y presentación de la problemática, se avanza en una aproximación al tema a partir de propuestas de teorías filosóficas y antropológicas contemporáneas. Se tiene en cuenta, particularmente, a John Austin. Luego, se realiza un análisis de algunos casos en un contexto histórico. En particular se trabajan la formula consecratoria de la eucaristía y el rito del bautismo. Finalmente, se trazan los aspectos conclusivos del artículo.

The State of the Nation Debate as part of the democratic parliamentary ritual: Rajoy and Rubalcaba discourse analysis, 2014

In this article we analyze the Debate on the State of the Nation 2014. The methodology consists in coding the speeches of the prime minister, Mariano Rajoy (PP) and the then opposition leader Alfredo Perez Rubalcaba (PSOE) through extracting word clouds, branched maps and word trees that have shown the most common concepts and premises. This preliminary analysis of two dimensions, quantitative and qualitative, makes it much easier and viable subsequent discourse analysis where we focus on the different types of arguments in the communicative act: claim/solution, circumstantial premises, goal premises, value premises, meansgoal premises, alternative options/addressing alternative options.

"Lancea", Lusitanian Word, and the "Lancienses" Ethnogenesis

Analysis of the word lancea, of Hispanic origin after Varro, and of place names, people´s names and personal names derived from it. It confirms that the spear was the most important weapon in the Bronze Age, belonging to the iuventus and used as heroic and divine symbol. This analysis confirms also the personality of the Lusitanians, a people related to the Celts but with more archaic archaeological, linguistic and cultural characteristics originated in the tradition of the Atlantic Bronze in the II millennium BC. It is also relevant to better know the organisation of Broze and Iron Age societies and the origin of Indo-Europeans peoples in Western Europe and of pre-Roman peoples of Iberia.

Look before you leap : the epistemic violence that sometimes hides behind the word "inclusion"

This paper demonstrates how Indigenous Studies is controlled in some Australian universities in ways that continue the marginalisation, denigration and exploitation of Indigenous peoples. Moreover, it shows how the engagement of white notions of “inclusion” can result in the maintenance of racism, systemic marginalisation, white race privilege and radicalised subjectivity. A case study will be utilised which draws from the experience of two Indigenous scholars who were invited to be part of a panel to review one Australian university’s plan and courses in Indigenous studies. The case study offers the opportunity to destabilise the relationships between oppression and privilege and the epistemology that maintains them. The paper argues for the need to examine exactly what is being offered when universities provide opportunities for “inclusion”.

Word Segmentation for Chinese Wikipedia Using N-Gram Mutual Information

In this paper, we propose an unsupervised segmentation approach, named "n-gram mutual information", or NGMI, which is used to segment Chinese documents into n-character words or phrases, using language statistics drawn from the Chinese Wikipedia corpus. The approach alleviates the tremendous effort that is required in preparing and maintaining the manually segmented Chinese text for training purposes, and manually maintaining ever expanding lexicons. Previously, mutual information was used to achieve automated segmentation into 2-character words. The NGMI approach extends the approach to handle longer n-character words. Experiments with heterogeneous documents from the Chinese Wikipedia collection show good results.

Word-perfect Gatsby overcomes its flaws

Review of 'Gatz', Elevator Repair Company / Brisbane Powerhouse, published in The Australian, 12 May 2009.

Thai word segmentation with hidden Markov Model and decision tree

The Thai written language is one of the languages that does not have word boundaries. In order to discover the meaning of the document, all texts must be separated into syllables, words, sentences, and paragraphs. This paper develops a novel method to segment the Thai text by combining a non-dictionary based technique with a dictionary-based technique. This method first applies the Thai language grammar rules to the text for identifying syllables. The hidden Markov model is then used for merging possible syllables into words. The identified words are verified with a lexical dictionary and a decision tree is employed to discover the words unidentified by the lexical dictionary. Documents used in the litigation process of Thai court proceedings have been used in experiments. The results which are segmented words, obtained by the proposed method outperform the results obtained by other existing methods.

Improvement to Chinese information retrieval by incorporating word segmentation and query expansion

The increasing diversity of the Internet has created a vast number of multilingual resources on the Web. A huge number of these documents are written in various languages other than English. Consequently, the demand for searching in non-English languages is growing exponentially. It is desirable that a search engine can search for information over collections of documents in other languages. This research investigates the techniques for developing high-quality Chinese information retrieval systems. A distinctive feature of Chinese text is that a Chinese document is a sequence of Chinese characters with no space or boundary between Chinese words. This feature makes Chinese information retrieval more difficult since a retrieved document which contains the query term as a sequence of Chinese characters may not be really relevant to the query since the query term (as a sequence Chinese characters) may not be a valid Chinese word in that documents. On the other hand, a document that is actually relevant may not be retrieved because it does not contain the query sequence but contains other relevant words. In this research, we propose two approaches to deal with the problems. In the first approach, we propose a hybrid Chinese information retrieval model by incorporating word-based techniques with the traditional character-based techniques. The aim of this approach is to investigate the influence of Chinese segmentation on the performance of Chinese information retrieval. Two ranking methods are proposed to rank retrieved documents based on the relevancy to the query calculated by combining character-based ranking and word-based ranking. Our experimental results show that Chinese segmentation can improve the performance of Chinese information retrieval, but the improvement is not significant if it incorporates only Chinese segmentation with the traditional character-based approach. In the second approach, we propose a novel query expansion method which applies text mining techniques in order to find the most relevant words to extend the query. Unlike most existing query expansion methods, which generally select the highly frequent indexing terms from the retrieved documents to expand the query. In our approach, we utilize text mining techniques to find patterns from the retrieved documents that highly correlate with the query term and then use the relevant words in the patterns to expand the original query. This research project develops and implements a Chinese information retrieval system for evaluating the proposed approaches. There are two stages in the experiments. The first stage is to investigate if high accuracy segmentation can make an improvement to Chinese information retrieval. In the second stage, a text mining based query expansion approach is implemented and a further experiment has been done to compare its performance with the standard Rocchio approach with the proposed text mining based query expansion method. The NTCIR5 Chinese collections are used in the experiments. The experiment results show that by incorporating the text mining based query expansion with the hybrid model, significant improvement has been achieved in both precision and recall assessments.

The fan ritual scale and sports attendance

Rituals are an important part of society, and are a frequent topic of investigation among sociologists and anthropologists. Marketing applications of ritual, however, are rare. This study investigates the relationships between sports fan rituals, team identification and attendance using the Fan Ritual Scale. Data were collected at a professional football game in Australia. The results reveal a significant and positive relationship between social rituals, identification and attendance. There was no relationship however, between identification, attendance and personal rituals.

Changing lanes : an exploration of the journey from dance through choreography to directing and the spoken word

This paper reveals a journey of theatrical exploration. It is a journey of enquiry and investigation backed by a vigorous, direct and dense professional history of creative work.

The nonconceptual gateway to early word learning

My research investigates why nouns are learned disproportionately more frequently than other kinds of words during early language acquisition (Gentner, 1982; Gleitman, et al., 2004). This question must be considered in the context of cognitive development in general. Infants have two major streams of environmental information to make meaningful: perceptual and linguistic. Perceptual information flows in from the senses and is processed into symbolic representations by the primitive language of thought (Fodor, 1975). These symbolic representations are then linked to linguistic input to enable language comprehension and ultimately production. Yet, how exactly does perceptual information become conceptualized? Although this question is difficult, there has been progress. One way that children might have an easier job is if they have structures that simplify the data. Thus, if particular sorts of perceptual information could be separated from the mass of input, then it would be easier for children to refer to those specific things when learning words (Spelke, 1990; Pylyshyn, 2003). It would be easier still, if linguistic input was segmented in predictable ways (Gentner, 1982; Gleitman, et al., 2004) Unfortunately the frequency of patterns in lexical or grammatical input cannot explain the cross-cultural and cross-linguistic tendency to favor nouns over verbs and predicates. There are three examples of this failure: 1) a wide variety of nouns are uttered less frequently than a smaller number of verbs and yet are learnt far more easily (Gentner, 1982); 2) word order and morphological transparency offer no insight when you contrast the sentence structures and word inflections of different languages (Slobin, 1973) and 3) particular language teaching behaviors (e.g. pointing at objects and repeating names for them) have little impact on children's tendency to prefer concrete nouns in their first fifty words (Newport, et al., 1977). Although the linguistic solution appears problematic, there has been increasing evidence that the early visual system does indeed segment perceptual information in specific ways before the conscious mind begins to intervene (Pylyshyn, 2003). I argue that nouns are easier to learn because their referents directly connect with innate features of the perceptual faculty. This hypothesis stems from work done on visual indexes by Zenon Pylyshyn (2001, 2003). Pylyshyn argues that the early visual system (the architecture of the "vision module") segments perceptual data into pre-conceptual proto-objects called FINSTs. FINSTs typically correspond to physical things such as Spelke objects (Spelke, 1990). Hence, before conceptualization, visual objects are picked out by the perceptual system demonstratively, like a finger pointing indicating ‘this’ or ‘that’. I suggest that this primitive system of demonstration elaborates on Gareth Evan's (1982) theory of nonconceptual content. Nouns are learnt first because their referents attract demonstrative visual indexes. This theory also explains why infants less often name stationary objects such as plate or table, but do name things that attract the focal attention of the early visual system, i.e., small objects that move, such as ‘dog’ or ‘ball’. This view leaves open the question how blind children learn words for visible objects and why children learn category nouns (e.g. 'dog'), rather than proper nouns (e.g. 'Fido') or higher taxonomic distinctions (e.g. 'animal').

Entertaining the E Word : Barrie Kosky's On Ecstasy [A Review]

A review of Barrie Kosky's essay, On Ecstasy : Most of us describe the E word as a pleasant, out of this world experience—a type of boundless, artificial joy, deliberately induced by some kind of technicoloured drug. For others, it is that “lovey dovey” feeling. A spinning ceiling. Anything Lindt. For sensualist and soup connoisseur Barrie Kosky, it is easier than this. Being On Ecstasy involves, quite simply, his grandmother's chicken specialty—something warm and golden, surrendered with vegetables and a side of transcendental bliss. “A soup that took you to the beginning and end of time itself. A dazzling, pure, clear rhapsody” (7).

Modelling word meaning using efficient tensor representations

Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these models has been raised as a weakness when performing cognitive tasks. This paper presents an efficient tensor based approach to modelling word meaning that builds on recent attempts to encode word order information, while providing flexible methods for extracting task specific semantic information.