Combined optimization of feature selection and algorithm parameters in machine learning of language

Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algorithms have the 'right bias' to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology currently used in comparative machine learning experiments, the results may often not be reliable because of the role of and interaction between feature selection and algorithm parameter optimization. We propose genetic algorithms as a practical approach to achieve both higher accuracy within a single approach, and more reliable comparisons

Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation

The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest. In contrast to human intelligence, these methods lack versatility and struggle to learn and adapt quickly to new tasks, where labeled data is scarce. Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples. In this paper, we propose a meta-learning framework for few-shot word sense disambiguation (WSD), where the goal is to learn to disambiguate unseen words from only a few labeled instances. Meta-learning approaches have so far been typically tested in an $N$-way, $K$-shot classification setting where each task has $N$ classes with $K$ examples per class. Owing to its nature, WSD deviates from this controlled setup and requires the models to handle a large number of highly unbalanced classes. We extend several popular meta-learning approaches to this scenario, and analyze their strengths and weaknesses in this new challenging setting.Comment: Added additional experiment

A Neuro-Evolutionary Corpus-Based Method for Word Sense Disambiguation

International audienceWe propose a supervised approach to Word Sense Disambiguation based on Neural Networks combined with Evolutionary Algorithms. An established method to automatically design the structure and learn the connection weights of Neural Networks by means of an Evolutionary Algorithm is used to evolve a neural-network disambiguator for each polysemous word, against a dataset extracted from an annotated corpus. Two distributed encoding schemes, based on the orthography of words and characterized by different degrees of information compression, have been used to represent the context in which a word occurs. The performance of such encoding schemes has been compared. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. Comparison with the best entry of the Semeval-2007 competition has shown that the proposed approach is almost competitive with state-of-the-art WSD approaches

Gujarati Word Sense Disambiguation using Genetic Algorithm

Genetic algorithms (GAs) have widely been investigated to solve hard optimization problems, including the word sense disambiguation (WSD). This problem asks to determine which sense of a polysemous word is used in a given context. Several approaches have been investigated for WSD in English, French, German and some Indo-Aryan languages like Hindi, Marathi, Malayalam, etc. however, research on WSD in Guajarati Language is relatively limited. In this paper, an approach for Guajarati WSD using Genetic algorithm has been proposed which uses Knowledge based approach where Indo-Aryan WordNet for Guajarati is used as lexical database for WSD

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Inférences réflexives dans la publicité

Advertisements are so ubiquitous nowadays that capturing the addressee’s attention and maintaining it long enough for them to be fully processed have become fundamental objectives for advertisers. Employing specific strategies in the design of the advertisement contributes efficiently to achieving these goals, getting the audience not only to attend the stimulus but also to process it in certain ways favourable for the advertiser. We argue that Relevance theory, an approach to communication built on a massively modular view of cognition, offers the right tools to explain the nature of the interpretative processes in verbal comprehension. Knowledge of the relevance-based reflexive inferential procedures involved in utterance interpretation allows advertisers to foresee the addressee’s processing behaviour, giving them the possibility to control it in a such a way that the intended interpretative effects are achieved in the desired way

Evolution, communication, and the proper function of language

Language is both a biological and a cultural phenomenon. Our aim here is to discuss, in an evolutionary perspective, the articulation of these two aspects of language. For this, we draw on the general conceptual framework developed by Ruth Millikan (1984) while at the same time dissociating ourselves from her view of language