Classifying paragraph types using linguistic features: Is paragraph positioning important?

Classifying paragraph types using linguistic features: Is paragraph positioning important?

This study examines the potential for computational tools and human raters to classify paragraphs based on positioning. In this study, a corpus of 182 paragraphs was collected from student, argumentative essays. The paragraphs selected were initial, middle, and final paragraphs and their positioning related to introductory, body, and concluding paragraphs. The paragraphs were analyzed by the co...

Introductory paragraph

Using linguistically motivated features for paragraph boundary identification

In this paper we propose a machinelearning approach to paragraph boundary identification which utilizes linguistically motivated features. We investigate the relation between paragraph boundaries and discourse cues, pronominalization and information structure. We test our algorithm on German data and report improvements over three baselines including a reimplementation of Sporleder & Lapata’s (...

Generative Paragraph Vector

The recently introduced Paragraph Vector is an efficient method for learning highquality distributed representations for pieces of texts. However, an inherent limitation of Paragraph Vector is lack of ability to infer distributed representations for texts outside of the training set. To tackle this problem, we introduce a Generative Paragraph Vector, which can be viewed as a probabilistic exten...

Binary Paragraph Vectors

Recently Le & Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-ofthe-art distributed representations of documents. Inspired by this work, we present Binary Paragraph Vector models: simple neural networks that learn short binary codes for fast information retrieval. We show that binary paragraph vectors outperform autoencoder-based binary codes, d...