Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis

We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules. On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains. On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned. Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin. Experiments also show how the same compositional operations can be shared across languages. The system is available at http://www.grupolys.org/software/UUUSA/Comment: 19 pages, 5 Tables, 6 Figures. This is the authors version of a work that was accepted for publication in Knowledge-Based System

Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)

Generative grammar

Generative Grammar is the label of the most influential research program in linguistics and related fields in the second half of the 20. century. Initiated by a short book, Noam Chomsky's Syntactic Structures (1957), it became one of the driving forces among the disciplines jointly called the cognitive sciences. The term generative grammar refers to an explicit, formal characterization of the (largely implicit) knowledge determining the formal aspect of all kinds of language behavior. The program had a strong mentalist orientation right from the beginning, documented e.g. in a fundamental critique of Skinner's Verbal behavior (1957) by Chomsky (1959), arguing that behaviorist stimulus-response-theories could in no way account for the complexities of ordinary language use. The "Generative Enterprise", as the program was called in 1982, went through a number of stages, each of which was accompanied by discussions of specific problems and consequences within the narrower domain of linguistics as well as the wider range of related fields, such as ontogenetic development, psychology of language use, or biological evolution. Four stages of the Generative Enterprise can be marked off for expository purposes

Sentiment Analysis Using Deep Learning: A Comparison Between Chinese And English

With the increasing popularity of opinion-rich resources, opinion mining and sentiment analysis has received increasing attention. Sentiment analysis is one of the most effective ways to find the opinion of authors. By mining what people think, sentiment analysis can provide the basis for decision making. Most of the objects of analysis are text data, such as Facebook status and movie reviews. Despite many sentiment classification models having good performance on English corpora, they are not good at Chinese or other languages. Traditional sentiment approaches impose many restrictions on the raw data, and they don't have enough capacity to deal with long-distance sequential dependencies. So, we propose a model based on recurrent neural network model using a context vector space model. Chinese information entropy is typically higher than English, we therefore hypothesise that context vector space model can be used to improve the accuracy of sentiment analysis. Our algorithm represents each complex input by a dense vector trained to translate sequence data to another sequence, like the translation of English and French. Then we build a recurrent neural network with the Long-Short-Term Memory model to deal the long-distance dependencies in input data, such as movie review. The results show that our approach has promise but still has a lot of room for improvement

THE ROLE OF NON-LINGUISTIC COGNITIVE DEVELOPMENT AND LANGUAGE-SPECIFIC MORPHOLOGICAL PROPERTIES IN THE FIRST LANGUAGE ACQUISITION OF DEMONSTRATIVES

This dissertation investigates children’s comprehension of demonstratives, such as this and that in English. As deictic spatial expressions, the interpretation of demonstratives is context-dependent: a proximal demonstrative (e.g., this) picks out the entity near the speaker, while a distal demonstrative (e.g., that) picks out the entity apart from the speaker; crucially, the entity-speaker distance is determined by the speaker’s perspective, which varies across contexts. Studies have shown that children tend to be non-adult-like when comprehending demonstratives uttered by a speaker who has a different perspective from their own (e.g., Clark & Sengul, 1978; Zhao, 2007). To better understand children’s comprehension of demonstratives, this dissertation explores (i) the cognitive factors which might hinder children’s adult-like knowledge, and (ii) the language-specific factors which might improve children’s demonstrative comprehension. This dissertation first discusses Theory of Mind (ToM) and Executive Function (EF) and how the development of each may hinder children’s comprehension of demonstratives. Successful comprehension of demonstratives requires the listener to incorporate the speaker’s perspective, in which cognitive abilities may play a role. It has been suggested that children’s non-adult-like demonstratives may be related to their still-developing ToM (de Villiers, 2007) and EF (Nilsen & Graham, 2012). Two experiments directly tested this hypothesis with English-speaking and Chinese-speaking children, respectively. Both experiments utilized two demonstrative comprehension linguistic tasks, and two cognitive tasks measuring ToM and EF, respectively. The results from both experiments suggest that children’s successful comprehension of demonstratives may be related to their ToM development, but not EF. This dissertation then examines whether a language-specific morphological representation of demonstratives may interact with children’s comprehension in a way that prevents them from committing non-adult-like comprehension. Demonstratives in Mandarin Chinese are of particular interest because they typically occur with classifiers. Classifiers are semantically dependent on their associated referents; interestingly, classifiers are known to facilitate adults’ sentence processing (e.g., Hsu, 2006; Wu, Kaiser, & Andersen, 2009). Thus, this dissertation examined whether and to what extent the classifier may improve Chinese-speaking children’s demonstrative comprehension. Results reveal that the classifier semantics improves children’s demonstrative comprehension, particularly when the classifier semantics itself is sufficient to identify the referent. In sum, the results of the studies discussed in this dissertation suggest that both cognitive factors and language-specific factors play an important role in children’s demonstrative comprehension