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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small Dataset

verfasst von : Cornelia Ferner, Stefan Wegenkittl

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

This paper aims at identifying sequences of words related to specific product components in online product reviews. A reliable baseline performance for this topic classification problem is given by a Max Entropy classifier which assumes independence over subsequent topics. However, the reviews exhibit an inherent structure on the document level allowing to frame the task as sequence classification problem. Since more flexible models from the class of Conditional Random Fields were not competitive because of the limited amount of training data available, we propose using a Hidden Markov Model instead and decouple the training of transition and emission probabilities. The discriminating power of the Max Entropy approach is used for the latter. Besides outperforming both standalone methods as well as more generic models such as linear-chain Conditional Random Fields, the combined classifier is able to assign topics on sub-sentence level although labeling in the training data is only available on sentence level.

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Vorheriges Kapitel Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts
Nächstes Kapitel Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model
Fußnoten
1
This is why we call it a “semi-discriminative approach”.
 
3
The sentence IDs provided in [18] are neither consecutive nor contiguous.
 
4
The HMM algorithm is no longer supported in the sklearn library.
 
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Metadaten
Titel
A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small Dataset
verfasst von
Cornelia Ferner
Stefan Wegenkittl
Copyright-Jahr
2020
Buch
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-030-46146-1

Electronic ISBN: 978-3-030-46147-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-46147-8_42