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Improved Information Structure Analysis of Scientific Documents Through Discourse and Lexical Constraints

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Inferring the information structure of scientific documents is useful for many down-stream applications. Existing feature-based machine learning approaches to this task require substantial training data and suffer from limited performance. Our idea is to guide feature-based models with declarative domain knowledge encoded as posterior distribution constraints. We explore a rich set of discourse and lexical constraints which we incorporate through the Generalized Expectation (GE) criterion. Our constrained model improves the performance of existing fully and lightly supervised models. Even a fully unsupervised version of this model outperforms lightly supervised feature-based models, showing that our approach can be useful even when no labeled data is available.

This talk is part of the NLIP Seminar Series series.

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