5 research outputs found
Segmented Shape-Symbolic Time Series Representation
Abstract. This paper introduces a symbolic time series representation using monotonic sub-sequences and bottom up segmentation. The representation min-imizes the square error between the segments and their monotonic approximations. The representation can robustly classify the direction of a segment and is scale in-variant with respect to the time and value dimensions. This paper describes two experiments. The first shows how accurately the monotonic functions are able to discriminate between different segments. The second tests how well the segmenta-tion technique recognizes segments and classifies them with correct symbols. Fi-nally this paper illustrates the new representation on real-world data.
Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction
Training recurrent neural networks on long texts, in particular scholarly
documents, causes problems for learning. While hierarchical attention networks
(HANs) are effective in solving these problems, they still lose important
information about the structure of the text. To tackle these problems, we
propose the use of HANs combined with structure-tags which mark the role of
sentences in the document. Adding tags to sentences, marking them as
corresponding to title, abstract or main body text, yields improvements over
the state-of-the-art for scholarly document quality prediction. The proposed
system is applied to the task of accept/reject prediction on the PeerRead
dataset and compared against a recent BiLSTM-based model and joint
textual+visual model as well as against plain HANs. Compared to plain HANs,
accuracy increases on all three domains. On the computation and language domain
our new model works best overall, and increases accuracy 4.7% over the best
literature result. We also obtain improvements when introducing the tags for
prediction of the number of citations for 88k scientific publications that we
compiled from the Allen AI S2ORC dataset. For our HAN-system with
structure-tags we reach 28.5% explained variance, an improvement of 1.8% over
our reimplementation of the BiLSTM-based model as well as 1.0% improvement over
plain HANs.Comment: This new version of the paper brings the paper up-to-date with the
improved paper, published at the First Workshop on Scholarly Document
Processing, at EMNLP 2020. .Additionally, minor corrections were made
including addition of color to Figures 1,2. The changes in comparison to the
first arXiv version are substantial, including various additional results,
and substantial improvements to the tex