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The process of translating comprises in its essence the whole secret of human understanding and social communication. This chapter introduces techniques for machine translation (MT), the use of MACHINE TRANSLATION MT computers to automate some or all of the process of translating from one language to another. Translation, in its full generality, is a(More)
The natural language processing community has recently experienced a growth of interest in domain independent shallow semantic parsing – the process of assigning a WHO did WHAT to WHOM, WHEN, WHERE, WHY, HOW etc. structure to plain text. This process entails identifying groups of words in a sentence that represent these semantic arguments and assigning(More)
In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classifiers. We show performance improvements through a number of new(More)
Semantic role labeling is the process of annotating the predicate-argument structure in text with semantic labels. In this paper we present a state-of-the-art base-line semantic role labeling system based on Support Vector Machine classifiers. We show improvements on this system by: i) adding new features including features extracted from dependency parses,(More)
This paper describes a semantic role labeling system that uses features derived from different syntactic views, and combines them within a phrase-based chunk-ing paradigm. For an input sentence, syntactic constituent structure parses are generated by a Charniak parser and a Collins parser. Semantic role labels are assigned to the constituents of each parse(More)
An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology at all levels and with all modern technologies this text takes an empirical approach to the subject,(More)
17 DISCOURSE ¡ Gracie: Oh yeah.. . and then Mr. and Mrs. Jones were having matrimonial trouble, and my brother was hired to watch Mrs. Jones. George: Well, I imagine she was a very attractive woman. Gracie: She was, and my brother watched her day and night for six months. George: Well, what happened? Gracie: She finally got a divorce. Up to this point of(More)
Most research on semantic role labeling (SRL) has been focused on training and evaluating on the same corpus in order to develop the technology. This strategy, while appropriate for initiating research, can lead to over-training to the particular corpus. The work presented in this paper focuses on analyzing the robustness of an SRL system when trained on(More)
In this paper, we present a semantic role la-beler (or chunker) that groups syntactic chunks (i.e. base phrases) into the arguments of a predicate. This is accomplished by casting the semantic labeling as the classification of syntactic chunks (e.g. NP-chunk, PP-chunk) into one of several classes such as the beginning of an argument (B-ARG), inside an(More)