Keng-Te Liao


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Explainable and Sparse Representations of Academic Articles for Knowledge Exploration
Keng-Te Liao | Zhihong Shen | Chiyuan Huang | Chieh-Han Wu | PoChun Chen | Kuansan Wang | Shou-de Lin
Proceedings of the 28th International Conference on Computational Linguistics

We focus on a recently deployed system built for summarizing academic articles by concept tagging. The system has shown great coverage and high accuracy of concept identification which could be contributed by the knowledge acquired from millions of publications. Provided with the interpretable concepts and knowledge encoded in a pre-trained neural model, we investigate whether the tagged concepts can be applied to a broader class of applications. We propose transforming the tagged concepts into sparse vectors as representations of academic documents. The effectiveness of the representations is analyzed theoretically by a proposed framework. We also empirically show that the representations can have advantages on academic topic discovery and paper recommendation. On these applications, we reveal that the knowledge encoded in the tagging system can be effectively utilized and can help infer additional features from data with limited information.

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Explaining Word Embeddings via Disentangled Representation
Keng-Te Liao | Cheng-Syuan Lee | Zhong-Yu Huang | Shou-de Lin
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Disentangled representations have attracted increasing attention recently. However, how to transfer the desired properties of disentanglement to word representations is unclear. In this work, we propose to transform typical dense word vectors into disentangled embeddings featuring improved interpretability via encoding polysemous semantics separately. We also found the modular structure of our disentangled word embeddings helps generate more efficient and effective features for natural language processing tasks.


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Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings
Hong-You Chen | Cheng-Syuan Lee | Keng-Te Liao | Shou-De Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly.