Meng Wang


Multi-modal Contrastive Representation Learning for Entity Alignment
Zhenxi Lin | Ziheng Zhang | Meng Wang | Yinghui Shi | Xian Wu | Yefeng Zheng
Proceedings of the 29th International Conference on Computational Linguistics

Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with entities. Most previous works focus on how to utilize and encode information from different modalities, while it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity. In this paper, we propose MCLEA, a Multi-modal Contrastive Learning based Entity Alignment model, to obtain effective joint representations for multi-modal entity alignment. Different from previous works, MCLEA considers task-oriented modality and models the inter-modal relationships for each entity representation. In particular, MCLEA firstly learns multiple individual representations from multiple modalities, and then performs contrastive learning to jointly model intra-modal and inter-modal interactions. Extensive experimental results show that MCLEA outperforms state-of-the-art baselines on public datasets under both supervised and unsupervised settings.


Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews
Jianxing Yu | Zheng-Jun Zha | Meng Wang | Tat-Seng Chua
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews
Jianxing Yu | Zheng-Jun Zha | Meng Wang | Kai Wang | Tat-Seng Chua
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing


Automatic Acquisition of Chinese Novel Noun Compounds
Meng Wang | Chu-Ren Huang | Shiwen Yu | Weiwei Sun
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Automatic acquisition of novel compounds is notoriously difficult because most novel compounds have relatively low frequency in a corpus. The current study proposes a new method to deal with the novel compound acquisition challenge. We model this task as a two-class classification problem in which a candidate compound is either classified as a compound or a non-compound. A machine learning method using SVM, incorporating two types of linguistically motivated features: semantic features and character features, is applied to identify rare but valid noun compounds. We explore two kinds of training data: one is virtual training data which is obtained by three statistical scores, i.e. co-occurrence frequency, mutual information and dependent ratio, from the frequent compounds; the other is real training data which is randomly selected from the infrequent compounds. We conduct comparative experiments, and the experimental results show that even with limited direct evidence in the corpus for the novel compounds, we can make full use of the typical frequent compounds to help in the discovery of the novel compounds.


Chinese Semantic Role Labeling with Shallow Parsing
Weiwei Sun | Zhifang Sui | Meng Wang | Xin Wang
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

Prediction of Thematic Rank for Structured Semantic Role Labeling
Weiwei Sun | Zhifang Sui | Meng Wang
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers