We propose a deep learning model for identifying structure within experiment narratives in scientific literature. We take a sequence labeling approach to this problem, and label clauses within experiment narratives to identify the different parts of the experiment. Our dataset consists of paragraphs taken from open access PubMed papers labeled with rhetorical information as a result of our pilot annotation. Our model is a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells that labels clauses. The clause representations are computed by combining word representations using a novel attention mechanism that involves a separate RNN. We compare this model against LSTMs where the input layer has simple or no attention and a feature rich CRF model. Furthermore, we describe how our work could be useful for information extraction from scientific literature.
General domain discourse parsing is a well-studied problem. While there are many discourse theories (see @cite_8 , chapter 2 for an overview), Rhetorical Structure Theory (RST) by @cite_2 , received a lot of attention. It is generally accepted that relations between non-overlapping chunks of text need to be considered to account for the overall meaning . Accordingly, rhetorical relations are central in RST for marking the structure. In contrast, the taxonomy we use applies to the clauses themselves, instead of the relations between them. This is made possible by the specificity of our domain: In the general case, it may not be possible to identify the type of a clause in isolation. However, it has to be noted that the information conveyed by our clause-centric formalism may also be expressed using a relation-centric discourse formalism like RST. Figure shows one possible RST tree for the text shown in Figure .
@cite_2 @cite_8
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Rhetorical Structure Theory (RST) is a theory of text structure that is being extended to serve as a theoretical basis for computational text planning. Text structures in RST are hierarchic, built on small patterns called schemas. The schemas which compose the structural hierarchy of a text describe the functions of the parts rather than their form characteristics. Relations between text parts, comparable to conjunctive relations, are a prominent part of RST’s definitional machinery. Recent work has put RST onto a new definitional basis. This paper details the current status of descriptive RST, along with efforts to create a constructive version for use as a basis for programming a text planner. Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers can improve document-level sentiment analysis, via composition of local information up the discourse tree. First, we show that reweighting discourse units according to their position in a dependency representation of the rhetorical structure can yield substantial improvements on lexicon-based sentiment analysis. Next, we present a recursive neural network over the RST structure, which offers significant improvements over classificationbased methods.