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Zhenghua Li

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

FGNet: Leveraging Feature-Guided Attention to Refine SAM2 for 3D EM Neuron Segmentation

Zhenghua Li
Hang Chen
Zihao Sun
Kai Li
Xiaolin Hu

Accurate segmentation of neural structures in Electron Microscopy (EM) images is paramount for neuroscience. However, this task is challenged by intricate morphologies, low signal-to-noise ratios, and scarce annotations, limiting the accuracy and generalization of existing methods. To address these challenges, we seek to leverage the priors learned by visual foundation models on a vast amount of natural images to better tackle this task. Specifically, we propose a novel framework that can effectively transfer knowledge from Segment Anything 2 (SAM2), which is pre-trained on natural images, to the EM domain. We first use SAM2 to extract powerful, general-purpose features. To bridge the domain gap, we introduce a Feature-Guided Attention module that leverages semantic cues from SAM2 to guide a lightweight encoder, the Fine-Grained Encoder (FGE), in focusing on these challenging regions. Finally, a dual-affinity decoder generates both coarse and refined affinity maps. Experimental results demonstrate that our method achieves performance comparable to state-of-the-art (SOTA) approaches with the SAM2 weights frozen. Upon further fine-tuning on EM data, our method significantly outperforms existing SOTA methods. This study validates that transferring representations pre-trained on natural images, when combined with targeted domain-adaptive guidance, can effectively address the specific challenges in neuron segmentation.

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AIJ Journal 2021 Journal Article

Dependency-based syntax-aware word representations

Meishan Zhang
Zhenghua Li
Guohong Fu
Min Zhang

Dependency syntax has been demonstrated highly useful for a number of natural language processing (NLP) tasks. Typical approaches of utilizing dependency syntax include Tree-RNN and Tree-Linearization, both of which exploit explicit 1-best tree outputs from a well-trained parser as inputs. However, these approaches may suffer from error propagation due to the inevitable errors contained in the 1-best tree outputs. In this work, we propose a novel approach to integrate dependency syntax without using the discrete tree outputs. The key idea is to use the intermediate hidden representations of a well-trained encoder-decoder dependency parser, which are referred to as Dependency-based Syntax-Aware Word Representations (Dep-SAWRs). Then, we simply concatenate such Dep-SAWRs with the conventional context-insensitive word embeddings to compose input word representations, without requiring to modify the model architecture of the downstream tasks. We evaluate the proposed method on four kinds of typical NLP tasks, including sentence classification, sentence matching, sequence labeling and machine translation. Experimental results show that the proposed approach is highly promising. On the one hand, it can utilize dependency syntax effectively, bringing consistently better performance on the four tasks compared with baselines without using syntax. On the other hand, the proposed method can outperform the Tree-RNN and Tree-Linearization approaches in most settings, and meanwhile are highly efficient in syntax integration. In addition, the proposed method would be easily extendable to encoding other structural attributes of language.

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IJCAI Conference 2020 Conference Paper

Fast and Accurate Neural CRF Constituency Parsing

Yu Zhang
Houquan Zhou
Zhenghua Li

Estimating probability distribution is one of the core issues in the NLP field. However, in both deep learning (DL) and pre-DL eras, unlike the vast applications of linear-chain CRF in sequence labeling tasks, very few works have applied tree-structure CRF to constituency parsing, mainly due to the complexity and inefficiency of the inside-outside algorithm. This work presents a fast and accurate neural CRF constituency parser. The key idea is to batchify the inside algorithm for loss computation by direct large tensor operations on GPU, and meanwhile avoid the outside algorithm for gradient computation via efficient back-propagation. We also propose a simple two-stage bracketing-then-labeling parsing approach to improve efficiency further. To improve the parsing performance, inspired by recent progress in dependency parsing, we introduce a new scoring architecture based on boundary representation and biaffine attention, and a beneficial dropout strategy. Experiments on PTB, CTB5. 1, and CTB7 show that our two-stage CRF parser achieves new state-of-the-art performance on both settings of w/o and w/ BERT, and can parse over 1, 000 sentences per second. We release our code at https: //github. com/yzhangcs/crfpar.

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IJCAI Conference 2019 Conference Paper

Self-attentive Biaffine Dependency Parsing

Ying Li
Zhenghua Li
Min Zhang
Rui Wang
Sheng Li
Luo Si

The current state-of-the-art dependency parsing approaches employ BiLSTMs to encode input sentences. Motivated by the success of the transformer-based machine translation, this work for the first time applies the self-attention mechanism to dependency parsing as the replacement of the BiLSTM-based encoders, leading to competitive performance on both English and Chinese benchmark data. Based on the detailed error analysis, we then combine the power of both BiLSTM and self-attention via model ensembles, demonstrating their complementary capability of capturing contextual information. Finally, we explore the recently proposed contextualized word representations as extra input features, and further improve the parsing performance.

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AIJ Journal 2019 Journal Article

Syntax-aware entity representations for neural relation extraction

Zhengqiu He
Wenliang Chen
Zhenghua Li
Wei Zhang
Hao Shao
Min Zhang

Distantly supervised relation extraction has been widely used to find novel relational facts between entities from text, and can be easily scaled to very large corpora. Previous studies on neural relation extraction treat this task as a multi-instance learning problem, and encode the sentences in low-dimensional spaces via neural networks. Although great progress has been made, they seldom consider the information represented by entities, which are of great significance to relation extraction. In this article, we propose several methods based on different tree-based models to learn syntax-aware entity representations for neural relation extraction. First, we encode the context of entities on dependency trees as sentence-level entity embedding based on tree-structured neural network models. Then, we utilize inter-sentence attention mechanism to obtain sentence bag level entity embedding over all sentences containing the specified entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification. Experimental results on a widely used real-world dataset indicate that our system performs better than the state-of-the-art systems of relation extraction.

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AAAI Conference 2019 Conference Paper

Syntax-Aware Neural Semantic Role Labeling

Qingrong Xia
Zhenghua Li
Min Zhang
Meishan Zhang
Guohong Fu
Rui Wang
Luo Si

Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic structures, traditional discrete-feature-based SRL approaches make heavy use of syntactic features. In contrast, deep-neural-network-based approaches usually encode the input sentence as a word sequence without considering the syntactic structures. In this work, we investigate several previous approaches for encoding syntactic trees, and make a thorough study on whether extra syntax-aware representations are beneficial for neural SRL models. Experiments on the benchmark CoNLL-2005 dataset show that syntax-aware SRL approaches can effectively improve performance over a strong baseline with external word representations from ELMo. With the extra syntax-aware representations, our approaches achieve new state-of-the-art 85. 6 F1 (single model) and 86. 6 F1 (ensemble) on the test data, outperforming the corresponding strong baselines with ELMo by 0. 8 and 1. 0, respectively. Detailed error analysis are conducted to gain more insights on the investigated approaches.

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AAAI Conference 2018 Conference Paper

SEE: Syntax-Aware Entity Embedding for Neural Relation Extraction

Zhengqiu He
Wenliang Chen
Zhenghua Li
Meishan Zhang
Wei Zhang
Min Zhang

Distant supervised relation extraction is an efﬁcient approach to scale relation extraction to very large corpora, and has been widely used to ﬁnd novel relational facts from plain text. Recent studies on neural relation extraction have shown great progress on this task via modeling the sentences in lowdimensional spaces, but seldom considered syntax information to model the entities. In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction. First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU. Then, we utilize both intra-sentence and inter-sentence attentions to obtain sentence set-level entity embedding over all sentences containing the focus entity pair. Finally, we combine both sentence embedding and entity embedding for relation classi- ﬁcation. We conduct experiments on a widely used real-world dataset and the experimental results show that our model can make full use of all informative instances and achieve stateof-the-art performance of relation extraction.

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