Aravind K. Joshi

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We present the second version of the Penn Discourse Treebank, PDTB-2.0, describing its lexically-grounded annotations of discourse relations and their two abstract object arguments over the 1 million word Wall Street Journal corpus. We describe all aspects of the annotation, including (a) the argument structure of discourse relations, (b) the sense(More)
This paper concerns relationships among focus of attention, choice of referring expression, and perceived coherence of utterances within a discourse segment. It presents a framework and initial theory of centering which are intended to model the local component of attentional state. The paper examines interactions between local coherence and choice of(More)
We consider the structural descriptions produced by various grammatical formalisms in terms of the complexity of the paths and the relationship between paths in the sets of structural descriptions that each system can generate. In considering the relationships between formalisms, we show that it is useful to abstract away from the details of the formalism,(More)
MAXIMUM ENTROPY MODELS FOR NATURAL LANGUAGE AMBIGUITY RESOLUTION Adwait Ratnaparkhi Supervisor: Professor Mitch Marcus This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state-of-the-art accuracies using a single statistical modeling technique based on the principle of maximum entropy. We discuss the(More)
This paper describes a new discourse-level annotation project – the Penn Discourse Treebank (PDTB) – that aims to produce a large-scale corpus in which discourse connectives are annotated, along with their arguments, thus exposing a clearly defined level of discourse structure. The PDTB is being built directly on top of the Penn Treebank and Propbank, thus(More)
In this paper, we propose guided learning, a new learning framework for bidirectional sequence classification. The tasks of learning the order of inference and training the local classifier are dynamically incorporated into a single Perceptron like learning algorithm. We apply this novel learning algorithm to POS tagging. It obtains an error rate of 2.67%(More)
The MIT Encyclopedia of the Cognitive Sciences (MITECS) brings together 471 brief articles on a very wide range of topics within cognitive science. The general editors worked with advisory editors in six contributing fields, including Gennaro Chierchia on Linguistics and Language and Michael I. Jordan and Stuart Russell on Computational Intelligence. MITECS(More)