tailieunhanh - Báo cáo khoa học: "Fast Semantic Extraction Using a Novel Neural Network Architecture"

We describe a novel neural network architecture for the problem of semantic role labeling. Many current solutions are complicated, consist of several stages and handbuilt features, and are too slow to be applied as part of real applications that require such semantic labels, partly because of their use of a syntactic parser (Pradhan et al., 2004; Gildea and Jurafsky, 2002). Our method instead learns a direct mapping from source sentence to semantic tags for a given predicate without the aid of a parser or a chunker. . | Fast Semantic Extraction Using a Novel Neural Network Architecture Ronan Collobert NEC Laboratories America Inc. 4 Independence Way Suite 200 Princeton NJ 08540 collober@ Jason Weston NEC Laboratories America Inc. 4 Independence Way Suite 200 Princeton NJ 08540 jasonw@ Abstract We describe a novel neural network architecture for the problem of semantic role labeling. Many current solutions are complicated consist of several stages and hand-built features and are too slow to be applied as part of real applications that require such semantic labels partly because of their use of a syntactic parser Pradhan et al. 2004 Gildea and Jurafsky 2002 . Our method instead learns a direct mapping from source sentence to semantic tags for a given predicate without the aid of a parser or a chunker. Our resulting system obtains accuracies comparable to the current state-of-the-art at a fraction of the computational cost. 1 Introduction Semantic understanding plays an important role in many end-user applications involving text for information extraction web-crawling systems question and answer based systems as well as machine translation summarization and search. Such applications typically have to be computationally cheap to deal with an enormous quantity of data . web-based systems process large numbers of documents whilst interactive human-machine applications require almost instant response. Another issue is the cost of producing labeled training data required for statistical models which is exacerbated when those models also depend on syntactic features which must themselves be learnt. To achieve the goal of semantic understanding the current consensus is to divide and conquer the 560 The company ARG0 bought REL sugar ARG1 on the world market ARGM-LOC to meet export commitments ARGM-PNC Figure 1 Example of Semantic Role Labeling from the PropBank dataset Palmer et al. 2005 . ARG0 is typically an actor REL an action ARG1 an object and ARGM describe .