Lan Du


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Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Jueqing Lu | Lan Du | Ming Liu | Joanna Dipnall
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification. The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations. Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III ) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.


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Leveraging Meta Information in Short Text Aggregation
He Zhao | Lan Du | Guanfeng Liu | Wray Buntine
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.


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Unsupervised Text Segmentation Based on Native Language Characteristics
Shervin Malmasi | Mark Dras | Mark Johnson | Lan Du | Magdalena Wolska
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most work on segmenting text does so on the basis of topic changes, but it can be of interest to segment by other, stylistically expressed characteristics such as change of authorship or native language. We propose a Bayesian unsupervised text segmentation approach to the latter. While baseline models achieve essentially random segmentation on our task, indicating its difficulty, a Bayesian model that incorporates appropriately compact language models and alternating asymmetric priors can achieve scores on the standard metrics around halfway to perfect segmentation.


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A Computationally Efficient Algorithm for Learning Topical Collocation Models
Zhendong Zhao | Lan Du | Benjamin Börschinger | John K Pate | Massimiliano Ciaramita | Mark Steedman | Mark Johnson
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Improving Topic Models with Latent Feature Word Representations
Dat Quoc Nguyen | Richard Billingsley | Lan Du | Mark Johnson
Transactions of the Association for Computational Linguistics, Volume 3

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.

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Using Entity Information from a Knowledge Base to Improve Relation Extraction
Lan Du | Anish Kumar | Mark Johnson | Massimiliano Ciaramita
Proceedings of the Australasian Language Technology Association Workshop 2015


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Topic Segmentation with a Structured Topic Model
Lan Du | Wray Buntine | Mark Johnson
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Modelling Sequential Text with an Adaptive Topic Model
Lan Du | Wray Buntine | Huidong Jin
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning