Zhiyi Luo


ExtRA: Extracting Prominent Review Aspects from Customer Feedback
Zhiyi Luo | Shanshan Huang | Frank F. Xu | Bill Yuchen Lin | Hanyuan Shi | Kenny Zhu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost every day. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.

Controlling Length in Abstractive Summarization Using a Convolutional Neural Network
Yizhu Liu | Zhiyi Luo | Kenny Zhu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Convolutional neural networks (CNNs) have met great success in abstractive summarization, but they cannot effectively generate summaries of desired lengths. Because generated summaries are used in difference scenarios which may have space or length constraints, the ability to control the summary length in abstractive summarization is an important problem. In this paper, we propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. The results show that this approach generates high-quality summaries with user defined length, and outperforms the baselines consistently in terms of ROUGE score, length variations and semantic similarity.


Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media
Bill Y. Lin | Frank Xu | Zhiyi Luo | Kenny Zhu
Proceedings of the 3rd Workshop on Noisy User-generated Text

In this paper, we present our multi-channel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on Noisy User-generated Text (W-NUT). We propose a novel approach, which incorporates comprehensive word representations with multi-channel information and Conditional Random Fields (CRF) into a traditional Bidirectional Long Short-Term Memory (BiLSTM) neural network without using any additional hand-craft features such as gazetteers. In comparison with other systems participating in the shared task, our system won the 2nd place.