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        <title>Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification</title>
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    <name type="personal">
        <namePart type="given">Puwei</namePart>
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    <name type="personal">
        <namePart type="given">Xiaoyong</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>NBSVM is one of the most popular methods for text classification and has been widely used as baselines for various text representation approaches. It uses Naive Bayes (NB) feature to weight sparse bag-of-n-grams representation. N-gram captures word order in short context and NB feature assigns more weights to those important words. However, NBSVM suffers from sparsity problem and is reported to be exceeded by newly proposed distributed (dense) text representations learned by neural networks. In this paper, we transfer the n-grams and NB weighting to neural models. We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words. In fact, our methods can be viewed as distributed (dense) counterparts of sparse bag-of-n-grams in NBSVM. We discover that n-grams and NB weighting are also effective in distributed representations. As a result, our models achieve new strong baselines on 9 text classification datasets, e.g. on IMDB dataset, we reach performance of 93.5% accuracy, which exceeds previous state-of-the-art results obtained by deep neural models. All source codes are publicly available at https://github.com/zhezhaoa/neural_BOW_toolkit.</abstract>
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%0 Conference Proceedings
%T Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification
%A Li, Bofang
%A Zhao, Zhe
%A Liu, Tao
%A Wang, Puwei
%A Du, Xiaoyong
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F li-etal-2016-weighted
%X NBSVM is one of the most popular methods for text classification and has been widely used as baselines for various text representation approaches. It uses Naive Bayes (NB) feature to weight sparse bag-of-n-grams representation. N-gram captures word order in short context and NB feature assigns more weights to those important words. However, NBSVM suffers from sparsity problem and is reported to be exceeded by newly proposed distributed (dense) text representations learned by neural networks. In this paper, we transfer the n-grams and NB weighting to neural models. We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words. In fact, our methods can be viewed as distributed (dense) counterparts of sparse bag-of-n-grams in NBSVM. We discover that n-grams and NB weighting are also effective in distributed representations. As a result, our models achieve new strong baselines on 9 text classification datasets, e.g. on IMDB dataset, we reach performance of 93.5% accuracy, which exceeds previous state-of-the-art results obtained by deep neural models. All source codes are publicly available at https://github.com/zhezhaoa/neural_BOW_toolkit.
%U https://aclanthology.org/C16-1150
%P 1591-1600

Markdown (Informal)

[Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification](https://aclanthology.org/C16-1150) (Li et al., COLING 2016)

• Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification (Li et al., COLING 2016)

ACL

• Bofang Li, Zhe Zhao, Tao Liu, Puwei Wang, and Xiaoyong Du. 2016. Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1591–1600, Osaka, Japan. The COLING 2016 Organizing Committee.