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Muhammad Asif Ali

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4 papers

1 author row

IJCAI Conference 2025 Conference Paper

ABNet: Mitigating Sample Imbalance in Anomaly Detection Within Dynamic Graphs

Yifan Hong
Muhammad Asif Ali
Huan Wang
Junyang Chen
Di Wang

In dynamic graphs, detecting anomalous nodes faces challenges due to sample imbalance, stemming from the scarcity of anomalous samples and feature representation bias. Existing methods often use unsupervised or semi-supervised learning to extract anomalous samples from unlabeled data, but struggle to obtain enough anomalous instances due to their low occurrence. Moreover, GNN-based approaches often prioritize normal samples, neglecting rare anomalies. To address these issues, we propose the Anomaly Balance Network (ABNet), designed to alleviate sample imbalance and enhance anomaly detection. ABNet includes three key components: a feature extractor that compares node features across time points to avoid bias, an anomaly augmenter that amplifies anomaly details and generates diverse anomalous samples, and an anomaly detector using meta-learning to adapt to graph evolution. Experimental results show that ABNet outperforms existing methods on three real-world datasets, effectively addressing sample imbalance.

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AAAI Conference 2020 Conference Paper

Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

Muhammad Asif Ali
Yifang Sun
Bing Li
Wei Wang

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention’s sentence-speciﬁc context. This is inadequate for highly overlapping and/or noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that reﬁnes the noisy mention representations by attending over corpus-level contextual clues prior to the end classiﬁcation. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10. 2% and 8. 3% for macro-f1 and micro-f1 respectively.

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AAAI Conference 2020 Conference Paper

GraphER: Token-Centric Entity Resolution with Graph Convolutional Neural Networks

Bing Li
Wei Wang
Yifang Sun
Linhan Zhang
Muhammad Asif Ali
Yi Wang

Entity resolution (ER) aims to identify entity records that refer to the same real-world entity, which is a critical problem in data cleaning and integration. Most of the existing models are attribute-centric, that is, matching entity pairs by comparing similarities of pre-aligned attributes, which require the schemas of records to be identical and are too coarse-grained to capture subtle key information within a single attribute. In this paper, we propose a novel graph-based ER model GraphER. Our model is token-centric: the ﬁnal matching results are generated by directly aggregating token-level comparison features, in which both the semantic and structural information has been softly embedded into token embeddings by training an Entity Record Graph Convolutional Network (ER-GCN). To the best of our knowledge, our work is the ﬁrst effort to do token-centric entity resolution with the help of GCN in entity resolution task. Extensive experiments on two real-world datasets demonstrate that our model stably outperforms state-of-the-art models.

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AAAI Conference 2019 Conference Paper

Antonym-Synonym Classification Based on New Sub-Space Embeddings

Muhammad Asif Ali
Yifang Sun
Xiaoling Zhou
Wei Wang
Xiang Zhao

Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance.

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