9,903 research outputs found
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
We consider the task of fine-grained sentiment analysis from the perspective
of multiple instance learning (MIL). Our neural model is trained on document
sentiment labels, and learns to predict the sentiment of text segments, i.e.
sentences or elementary discourse units (EDUs), without segment-level
supervision. We introduce an attention-based polarity scoring method for
identifying positive and negative text snippets and a new dataset which we call
SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating
MIL-style sentiment models like ours. Experimental results demonstrate superior
performance against multiple baselines, whereas a judgement elicitation study
shows that EDU-level opinion extraction produces more informative summaries
than sentence-based alternatives.Comment: Final published version. Please cite using appropriate date (2018).
Link to journal:
http://www.transacl.org/ojs/index.php/tacl/article/view/1225/27
Dependency-based Convolutional Neural Networks for Sentence Embedding
In sentence modeling and classification, convolutional neural network
approaches have recently achieved state-of-the-art results, but all such
efforts process word vectors sequentially and neglect long-distance
dependencies. To exploit both deep learning and linguistic structures, we
propose a tree-based convolutional neural network model which exploit various
long-distance relationships between words. Our model improves the sequential
baselines on all three sentiment and question classification tasks, and
achieves the highest published accuracy on TREC.Comment: this paper has been accepted by ACL 201
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