Various Types of Learning with Types

International audienceThis paper suggests another look on already known grammatical inference approaches based on specialization strategies

How to Split Recursive Automata

International audienceIn this paper, we interpret in terms of operations applying on extended finite state automata some algorithms that have been specified on categorial grammars to learn subclasses of context-free languages. The algorithms considered implement "specialization strategies". This new perspective also helps to understand how it is possible to control the combinatorial explosion that specialization techniques have to face, thanks to a typing approach

Effective Spoken Language Labeling with Deep Recurrent Neural Networks

Understanding spoken language is a highly complex problem, which can be decomposed into several simpler tasks. In this paper, we focus on Spoken Language Understanding (SLU), the module of spoken dialog systems responsible for extracting a semantic interpretation from the user utterance. The task is treated as a labeling problem. In the past, SLU has been performed with a wide variety of probabilistic models. The rise of neural networks, in the last couple of years, has opened new interesting research directions in this domain. Recurrent Neural Networks (RNNs) in particular are able not only to represent several pieces of information as embeddings but also, thanks to their recurrent architecture, to encode as embeddings relatively long contexts. Such long contexts are in general out of reach for models previously used for SLU. In this paper we propose novel RNNs architectures for SLU which outperform previous ones. Starting from a published idea as base block, we design new deep RNNs achieving state-of-the-art results on two widely used corpora for SLU: ATIS (Air Traveling Information System), in English, and MEDIA (Hotel information and reservation in France), in French.Comment: 8 pages. Rejected from IJCAI 2017, good remarks overall, but slightly off-topic as from global meta-reviews. Recommendations: 8, 6, 6, 4. arXiv admin note: text overlap with arXiv:1706.0174

Learnability of Pregroup Grammars

International audienceThis paper investigates the learnability by positive examples in the sense of Gold of Pregroup Grammars. In a first part, Pregroup Grammars are presented and a new parsing strategy is proposed. Then, theoretical learnability and non-learnability results for subclasses of Pregroup Grammars are proved. In the last two parts, we focus on learning Pregroup Grammars from a special kind of input called feature-tagged examples. A learning algorithm based on the parsing strategy presented in the first part is given. Its validity is proved and its properties are examplified

Grammatical inference by specialization as a state splitting strategy

International audienceWe exhibit connexions beteween two already known learning algorithms developped in different backgrounds. This allows to show that learning classical (or AB) categorial grammars by specialization can be identified with a “state splitting” strategy, in a search space made of extended automata. It also leads to a new interpretation of why it is possible to learn categorial grammars from semantically typed (in Montague's sense) examples

Cascade evaluation of clustering algorithm

International audienceThis paper is about the evaluation of the results of clustering algorithms, and the comparison of such algorithms. We propose a new method based on the enrichment of a set of independent labeled datasets by the results of clustering, and the use of a supervised method to evaluate the interest of adding such new information to the datasets. We thus adapt the cascade generalization paradigm in the case where we combine an unsupervised and a supervised learner. We also consider the case where independent supervised learnings are performed on the different groups of data objects created by the clustering. We then conduct experiments using different supervised algorithms to compare various clustering algorithms. And we thus show that our proposed method exhibits a coherent behavior, pointing out, for example, that the algorithms based on the use of complex probabilistic models outperform algorithms based on the use of simpler models

SuSE : Subspace Selection embedded in an EM algorithm

National audienceSubspace clustering is an extension of traditional clustering that seeks to find clusters embedded in different subspaces within a dataset. This is a particularly important challenge with high dimensional data where the curse of dimensionality occurs. It also has the benefit of providing smaller descriptions of the clusters found. In this field, we show that using probabilistic models provides many advantages over other existing methods. In particular, we show that the difficult problem of the parameter settings of subspace clustering algorithms can be seen as a model selection problem in the framework of probabilistic models. It thus allows us to design a method that does not require any input parameter from the user. We also point out the interest in allowing the clusters to overlap. And finally, we show that it is well suited for detecting the noise that may exist in the data, and that this helps to provide a more understandable representation of the clusters found