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Data representation is a fundamental task in machine learning, which affects the performance of the whole machine learning system. In the past few years, with the rapid development of deep learning, the models for word embedding based on neural networks have brought new inspiration to the research of natural language processing. In this paper, two kinds of schemes for improving the Continuous Bag-of-Words...
The diachronic nature of broadcast news data leads to the problem of out-of-vocabulary (OOV) words in large vocabulary continuous speech recognition (LVCSR) systems. Analysis of OOV words reveals that a majority of them are proper names (PNs). However, PNs are important for automatic indexing of audio–video content and for obtaining reliable automatic transcriptions. In this paper, we focus on the...
Short text classification is a crucial task for information retrieval, social medial text categorization, and many other applications. In reality, due to the inherent sparsity and the limited information available in the short texts, learning and classifying short texts is a significant challenge. In this paper, we propose a new framework, WEFEST, which expands short texts using word embedding for...
We design a way to model apps as vectors, inspired by the recent deep learning approach to vectorization of words called word2vec. Our method relies on how users use apps. In particular, we visualize the time series of how each user uses mobile apps as a “document”, and apply the recent word2vec modeling on these documents, but the novelty is that the training context is carefully weighted by the...
The learner modeling is a cornerstone of the personalized interaction in any Technology Enhanced Learning (TEL). Based on ontology's, the development of the Semantic Web offers new opportunities and challenges in the design of a new generation of adaptive systems, It's in this context that is situated the scope of our research works, with the aim of integrating a new modeling vision and learner pattern...
A conditional model is introduced for triggering understanding actions that correct errors of frame hypothesization and composition. Experimental evidence is provided using the French MEDIA corpus that these models trained with automatic speech recognition hypotheses trigger effective corrections of more than half of the errors. The overall frame recall increases from 0.76 to 0.84 while precision...
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