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Hua Wu

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27

AAAI Conference 2026 Conference Paper

BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation

  • Yuhao Wang
  • Ruiyang Ren
  • Yucheng Wang
  • Jing Liu
  • Xin Zhao
  • Hua Wu
  • Haifeng Wang

With the rapid advancement of large language models (LLMs), retrieval-augmented generation (RAG) has emerged as a critical approach to supplement the inherent knowledge limitations of LLMs. However, due to the typically large volume of retrieved information, RAG tends to operate with long context lengths. From the perspective of entropy engineering, we identify unconstrained entropy growth and attention dilution due to long retrieval context as significant factors affecting RAG performance. In this paper, we propose the balanced entropy-engineered RAG (BEE-RAG) framework, which improves the adaptability of RAG systems to varying context lengths through the principle of entropy invariance. By leveraging balanced context entropy to reformulate attention dynamics, BEE-RAG separates attention sensitivity from context length, ensuring a stable entropy level. Building upon this, we introduce a zero-shot inference strategy for multi-importance estimation and a parameter-efficient adaptive fine-tuning mechanism to obtain the optimal balancing factor for different settings. Extensive experiments across multiple RAG tasks demonstrate the effectiveness of BEE-RAG.

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NeurIPS Conference 2025 Conference Paper

Residual Stream Analysis of Overfitting And Structural Disruptions

  • Quan Liu
  • Han Zhou
  • Wenquan Wu
  • Hua Wu
  • Sen Su

Ensuring that large language models (LLMs) remain both helpful and harmless poses a significant challenge: fine-tuning on repetitive safety datasets—where unsafe prompts are paired with standard refusal templates—often leads to \emph{false refusals}, in which benign queries are declined. We first quantify this effect, showing that safety data exhibits substantially lower token entropy ($H_{1}\approx9. 18$) and 2-gram diversity ($\approx$ 0. 048) compared to general instruction data ($H_{1}\approx12. 05$, 2-gram$\approx$0. 205). To uncover the root cause, we introduce \emph{FlowLens}, a stable PCA-based tool for residual-stream geometry analysis, and reveal that higher proportions of safety examples concentrate variance along a few components, reducing representational smoothness and driving false refusals (false refusal rate rises from 63\% to 84\% as safety data increases from 0\% to 40\%). Guided by these insights, we propose \emph{Variance Concentration Loss} (VCL), an auxiliary regularizer that penalizes excessive variance concentration in mid-layer residuals. Empirical results demonstrate that VCL reduces false refusals by over 35 percentage points while maintaining or improving performance on general benchmarks such as MMLU and GSM8K.

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IJCAI Conference 2023 Conference Paper

Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation

  • Yanrui Du
  • Jing Yan
  • Yan Chen
  • Jing Liu
  • Sendong Zhao
  • Qiaoqiao She
  • Hua Wu
  • Haifeng Wang

Recent research has revealed that deep neural networks often take dataset biases as a shortcut to make decisions rather than understand tasks, leading to failures in real-world applications. In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution of training data. In particular, we define the word highly co-occurring with a specific label as biased word, and the example containing biased word as biased example. Our analysis shows that biased examples are easier for models to learn, while at the time of prediction, biased words make a significantly higher contribution to the models' predictions, and models tend to assign predicted labels over-relying on the spurious correlation between words and labels. To mitigate models' over-reliance on the shortcut (i. e. spurious correlation), we propose a training strategy Less-Learn-Shortcut (LLS): our strategy quantifies the biased degree of the biased examples and down-weights them accordingly. Experimental results on Question Matching, Natural Language Inference and Sentiment Analysis tasks show that LLS is a task-agnostic strategy and can improve the model performance on adversarial data while maintaining good performance on in-domain data.

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JMLR Journal 2023 Journal Article

SQLFlow: An Extensible Toolkit Integrating DB and AI

  • Jun Zhou
  • Ke Zhang
  • Lin Wang
  • Hua Wu
  • Yi Wang
  • Chaochao Chen

Integrating AI algorithms into databases is an ongoing effort in both academia and industry. We introduce SQLFlow, a toolkit seamlessly combining data manipulations and AI operations that can be run locally or remotely. SQLFlow extends SQL syntax to support typical AI tasks including model training, inference, interpretation, and mathematical optimization. It is compatible with a variety of database management systems (DBMS) and AI engines, including MySQL, TiDB, MaxCompute, and Hive, as well as TensorFlow, scikit-learn, and XGBoost. Documentations and case studies are available at https://sqlflow.org. The source code and additional details can be found at https://github.com/sql-machine-learning/sqlflow. &copy JMLR 2023. ( edit, beta )

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AAAI Conference 2023 Conference Paper

Universal Information Extraction as Unified Semantic Matching

  • Jie Lou
  • Yaojie Lu
  • Dai Dai
  • Wei Jia
  • Hongyu Lin
  • Xianpei Han
  • Le Sun
  • Hua Wu

The challenge of information extraction (IE) lies in the diversity of label schemas and the heterogeneity of structures. Traditional methods require task-specific model design and rely heavily on expensive supervision, making them difficult to generalize to new schemas. In this paper, we decouple IE into two basic abilities, structuring and conceptualizing, which are shared by different tasks and schemas. Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing. In this way, USM can jointly encode schema and input text, uniformly extract substructures in parallel, and controllably decode target structures on demand. Empirical evaluation on 4 IE tasks shows that the proposed method achieves state-of-the-art performance under the supervised experiments and shows strong generalization ability in zero/few-shot transfer settings.

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TMLR Journal 2022 Journal Article

Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning

  • Fan Wang
  • Hao Tian
  • Haoyi Xiong
  • Hua Wu
  • Jie Fu
  • Yang Cao
  • Yu Kang
  • Haifeng Wang

Artificial neural networks (ANNs) are typically confined to accomplishing pre-defined tasks by learning a set of static parameters. In contrast, biological neural networks (BNNs) can adapt to various new tasks by continually updating the neural connections based on the inputs, which is aligned with the paradigm of learning effective learning rules in addition to static parameters, \textit{e.g.}, meta-learning. Among various biologically inspired learning rules, Hebbian plasticity updates the neural network weights using local signals without the guide of an explicit target function, thus enabling an agent to learn automatically without human efforts. However, typical plastic ANNs using a large amount of meta-parameters violate the nature of the genomics bottleneck and potentially deteriorate the generalization capacity. This work proposes a new learning paradigm decomposing those connection-dependent plasticity rules into neuron-dependent rules thus accommodating $\Theta(n^2)$ learnable parameters with only $\Theta(n)$ meta-parameters. We also thoroughly study the effect of different neural modulation on plasticity. Our algorithms are tested in challenging random 2D maze environments, where the agents have to use their past experiences to shape the neural connections and improve their performances for the future. The results of our experiment validate the following: 1. Plasticity can be adopted to continually update a randomly initialized RNN to surpass pre-trained, more sophisticated recurrent models, especially when coming to long-term memorization. 2. Following the genomics bottleneck, the proposed decomposed plasticity can be comparable to or even more effective than canonical plasticity rules in some instances.

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AAAI Conference 2021 Conference Paper

ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs

  • Fei Yu
  • Jiji Tang
  • Weichong Yin
  • Yu Sun
  • Hao Tian
  • Hua Wu
  • Haifeng Wang

We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects) across vision and language, which are essential to vision-language cross-modal tasks. Utilizing scene graphs of visual scenes, ERNIE-ViL constructs Scene Graph Prediction tasks, i. e. , Object Prediction, Attribute Prediction and Relationship Prediction tasks in the pre-training phase. Specifically, these prediction tasks are implemented by predicting nodes of different types in the scene graph parsed from the sentence. Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language. After pre-training on large scale image-text aligned datasets, we validate the effectiveness of ERNIE-ViL on 5 cross-modal downstream tasks. ERNIE-ViL achieves state-of-the-art performances on all these tasks and ranks the first place on the VCR leaderboard with an absolute improvement of 3. 7%.

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NeurIPS Conference 2021 Conference Paper

Learning with Noisy Correspondence for Cross-modal Matching

  • Zhenyu Huang
  • Guocheng Niu
  • Xiao Liu
  • Wenbiao Ding
  • Xinyan Xiao
  • Hua Wu
  • Xi Peng

Cross-modal matching, which aims to establish the correspondence between two different modalities, is fundamental to a variety of tasks such as cross-modal retrieval and vision-and-language understanding. Although a huge number of cross-modal matching methods have been proposed and achieved remarkable progress in recent years, almost all of these methods implicitly assume that the multimodal training data are correctly aligned. In practice, however, such an assumption is extremely expensive even impossible to satisfy. Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels. Different from the traditional noisy labels which mainly refer to the errors in category labels, our noisy correspondence refers to the mismatch paired samples. To solve this new problem, we propose a novel method for learning with noisy correspondence, named Noisy Correspondence Rectifier (NCR). In brief, NCR divides the data into clean and noisy partitions based on the memorization effect of neural networks and then rectifies the correspondence via an adaptive prediction model in a co-teaching manner. To verify the effectiveness of our method, we conduct experiments by using the image-text matching as a showcase. Extensive experiments on Flickr30K, MS-COCO, and Conceptual Captions verify the effectiveness of our method. The code could be accessed from www. pengxi. me.

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IJCAI Conference 2021 Conference Paper

Weakly Supervised Dense Video Captioning via Jointly Usage of Knowledge Distillation and Cross-modal Matching

  • Bofeng Wu
  • Guocheng Niu
  • Jun Yu
  • Xinyan Xiao
  • Jian Zhang
  • Hua Wu

This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation. First, we adopt the knowledge distilled from relevant and well solved tasks to generate high-quality event proposals. Then we incorporate contrastive loss and cycle-consistency loss typically applied to cross-modal retrieval tasks to build semantic matching between the proposals and sentences, which are eventually used to train the caption generation module. In addition, the parameters of matching module are initialized via pre-training based on annotated images to improve the matching performance. Extensive experiments on ActivityNet-Caption dataset reveal the significance of distillation-based event proposal generation and cross-modal retrieval-based semantic matching to weakly supervised DVC, and demonstrate the superiority of our method to existing state-of-the-art methods.

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IJCAI Conference 2020 Conference Paper

Enhancing Dialog Coherence with Event Graph Grounded Content Planning

  • Jun Xu
  • Zeyang Lei
  • Haifeng Wang
  • Zheng-Yu Niu
  • Hua Wu
  • Wanxiang Che

How to generate informative, coherent and sustainable open-domain conversations is a non-trivial task. Previous work on knowledge grounded conversation generation focus on improving dialog informativeness with little attention on dialog coherence. In this paper, to enhance multi-turn dialog coherence, we propose to leverage event chains to help determine a sketch of a multi-turn dialog. We first extract event chains from narrative texts and connect them as a graph. We then present a novel event graph grounded Reinforcement Learning (RL) framework. It conducts high-level response content (simply an event) planning by learning to walk over the graph, and then produces a response conditioned on the planned content. In particular, we devise a novel multi-policy decision making mechanism to foster a coherent dialog with both appropriate content ordering and high contextual relevance. Experimental results indicate the effectiveness of this framework in terms of dialog coherence and informativeness.

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AAAI Conference 2020 Conference Paper

ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding

  • Yu Sun
  • Shuohuan Wang
  • Yukun Li
  • Shikun Feng
  • Hao Tian
  • Hua Wu
  • Haifeng Wang

Recently pre-trained models have achieved state-of-the-art results in various language understanding tasks. Current pretraining procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides co-occurring information, there exists other valuable lexical, syntactic and semantic information in training corpora, such as named entities, semantic closeness and discourse relations. In order to extract the lexical, syntactic and semantic information from training corpora, we propose a continual pre-training framework named ERNIE 2. 0 which incrementally builds pre-training tasks and then learn pre-trained models on these constructed tasks via continual multi-task learning. Based on this framework, we construct several tasks and train the ERNIE 2. 0 model to capture lexical, syntactic and semantic aspects of information in the training data. Experimental results demonstrate that ERNIE 2. 0 model outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several similar tasks in Chinese. The source codes and pre-trained models have been released at https: //github. com/PaddlePaddle/ERNIE.

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IJCAI Conference 2020 Conference Paper

ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation

  • Dongling Xiao
  • Han Zhang
  • Yukun Li
  • Yu Sun
  • Hao Tian
  • Hua Wu
  • Haifeng Wang

Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks. To address this issue, we propose an enhanced multi-flow sequence to sequence pre-training and fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method. To make generation closer to human writing patterns, this framework introduces a span-by-span generation flow that trains the model to predict semantically-complete spans consecutively rather than predicting word by word. Unlike existing pre-training methods, ERNIE-GEN incorporates multi-granularity target sampling to construct pre-training data, which enhances the correlation between encoder and decoder. Experimental results demonstrate that ERNIE-GEN achieves state-of-the-art results with a much smaller amount of pre-training data and parameters on a range of language generation tasks, including abstractive summarization (Gigaword and CNN/DailyMail), question generation (SQuAD), dialogue generation (Persona-Chat) and generative question answering (CoQA). The source codes and pre-trained models have been released at https: //github. com/PaddlePaddle/ERNIE/ernie-gen.

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AAAI Conference 2020 Conference Paper

Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation

  • Jun Xu
  • Haifeng Wang
  • Zhengyu Niu
  • Hua Wu
  • Wanxiang Che

Previous neural models on open-domain conversation generation have no effective mechanisms to manage chatting topics, and tend to produce less coherent dialogs. Inspired by the strategies in human-human dialogs, we divide the task of multi-turn open-domain conversation generation into two sub-tasks: explicit goal (chatting about a topic) sequence planning and goal completion by topic elaboration. To this end, we propose a three-layer Knowledge aware Hierarchical Reinforcement Learning based Model (KnowHRL). Specifically, for the first sub-task, the upper-layer policy learns to traverse a knowledge graph (KG) in order to plan a high-level goal sequence towards a good balance between dialog coherence and topic consistency with user interests. For the second sub-task, the middle-layer policy and the lower-layer one work together to produce an in-depth multi-turn conversation about a single topic with a goal-driven generation mechanism. The capability of goal-sequence planning enables chatbots to conduct proactive open-domain conversations towards recommended topics, which has many practical applications. Experiments demonstrate that our model outperforms state of the art baselines in terms of user-interest consistency, dialog coherence, and knowledge accuracy.

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AAAI Conference 2020 Conference Paper

Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding

  • Yuchen Liu
  • Jiajun Zhang
  • Hao Xiong
  • Long Zhou
  • Zhongjun He
  • Hua Wu
  • Haifeng Wang
  • Chengqing Zong

Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years. Compared to the traditional pipeline system, the end-to-end ST model has potential benefits of lower latency, smaller model size, and less error propagation. However, it is notoriously difficult to implement such a model without transcriptions as intermediate. Existing works generally apply multi-task learning to improve translation quality by jointly training end-to-end ST along with automatic speech recognition (ASR). However, different tasks in this method cannot utilize information from each other, which limits the improvement. Other works propose a two-stage model where the second model can use the hidden state from the first one, but its cascade manner greatly affects the ef- ficiency of training and inference process. In this paper, we propose a novel interactive attention mechanism which enables ASR and ST to perform synchronously and interactively in a single model. Specifically, the generation of transcriptions and translations not only relies on its previous outputs but also the outputs predicted in the other task. Experiments on TED speech translation corpora have shown that our proposed model can outperform strong baselines on the quality of speech translation and achieve better speech recognition performances as well.

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AAAI Conference 2019 Conference Paper

Addressing the Under-Translation Problem from the Entropy Perspective

  • Yang Zhao
  • Jiajun Zhang
  • Chengqing Zong
  • Zhongjun He
  • Hua Wu

Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance in recent years. However, the under-translation problem still remains a big challenge. In this paper, we focus on the under-translation problem and attempt to find out what kinds of source words are more likely to be ignored. Through analysis, we observe that a source word with a large translation entropy is more inclined to be dropped. To address this problem, we propose a coarse-to-fine framework. In coarse-grained phase, we introduce a simple strategy to reduce the entropy of highentropy words through constructing the pseudo target sentences. In fine-grained phase, we propose three methods, including pre-training method, multitask method and two-pass method, to encourage the neural model to correctly translate these high-entropy words. Experimental results on various translation tasks show that our method can significantly improve the translation quality and substantially reduce the under-translation cases of high-entropy words.

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IJCAI Conference 2019 Conference Paper

Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection

  • Chaotao Chen
  • Jinhua Peng
  • Fan Wang
  • Jun Xu
  • Hua Wu

In human conversation an input post is open to multiple potential responses, which is typically regarded as a one-to-many problem. Promising approaches mainly incorporate multiple latent mechanisms to build the one-to-many relationship. However, without accurate selection of the latent mechanism corresponding to the target response during training, these methods suffer from a rough optimization of latent mechanisms. In this paper, we propose a multi-mapping mechanism to better capture the one-to-many relationship, where multiple mapping modules are employed as latent mechanisms to model the semantic mappings from an input post to its diverse responses. For accurate optimization of latent mechanisms, a posterior mapping selection module is designed to select the corresponding mapping module according to the target response for further optimization. We also introduce an auxiliary matching loss to facilitate the optimization of posterior mapping selection. Empirical results demonstrate the superiority of our model in generating multiple diverse and informative responses over the state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

Learning to Select Knowledge for Response Generation in Dialog Systems

  • Rongzhong Lian
  • Min Xie
  • Fan Wang
  • Jinhua Peng
  • Hua Wu

End-to-end neural models for intelligent dialogue systems suffer from the problem of generating uninformative responses. Various methods were proposed to generate more informative responses by leveraging external knowledge. However, few previous work has focused on selecting appropriate knowledge in the learning process. The inappropriate selection of knowledge could prohibit the model from learning to make full use of the knowledge. Motivated by this, we propose an end-to-end neural model which employs a novel knowledge selection mechanism where both prior and posterior distributions over knowledge are used to facilitate knowledge selection. Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process. Meanwhile, a prior distribution, which is inferred from utterances only, is used to approximate the posterior distribution so that appropriate knowledge can be selected even without responses during the inference process. Compared with the previous work, our model can better incorporate appropriate knowledge in response generation. Experiments on both automatic and human evaluation verify the superiority of our model over previous baselines.

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AAAI Conference 2019 Conference Paper

Modeling Coherence for Discourse Neural Machine Translation

  • Hao Xiong
  • Zhongjun He
  • Hua Wu
  • Haifeng Wang

Discourse coherence plays an important role in the translation of one text. However, the previous reported models most focus on improving performance over individual sentence while ignoring cross-sentence links and dependencies, which affects the coherence of the text. In this paper, we propose to use discourse context and reward to refine the translation quality from the discourse perspective. In particular, we generate the translation of individual sentences at first. Next, we deliberate the preliminary produced translations, and train the model to learn the policy that produces discourse coherent text by a reward teacher. Practical results on multiple discourse test datasets indicate that our model significantly improves the translation quality over the state-of-the-art baseline system by +1. 23 BLEU score. Moreover, our model generates more discourse coherent text and obtains +2. 2 BLEU improvements when evaluated by discourse metrics.

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AAAI Conference 2018 Conference Paper

Multi-Channel Encoder for Neural Machine Translation

  • Hao Xiong
  • Zhongjun He
  • Xiaoguang Hu
  • Hua Wu

Attention-based Encoder-Decoder has the effective architecture for neural machine translation (NMT), which typically relies on recurrent neural networks (RNN) to build the blocks that will be lately called by attentive reader during the decoding process. This design of encoder yields relatively uniform composition on source sentence, despite the gating mechanism employed in encoding RNN. On the other hand, we often hope the decoder to take pieces of source sentence at varying levels suiting its own linguistic structure: for example, we may want to take the entity name in its raw form while taking an idiom as a perfectly composed unit. Motivated by this demand, we propose Multi-channel Encoder (MCE), which enhances encoding components with different levels of composition. More specifically, in addition to the hidden state of encoding RNN, MCE takes 1) the original word embedding for raw encoding with no composition, and 2) a particular design of external memory in Neural Turing Machine (NTM) for more complex composition, while all three encoding strategies are properly blended during decoding. Empirical study on Chinese-English translation shows that our model can improve by 6. 52 BLEU points upon a strong open source NMT system: DL4MT1. On the WMT14 English- French task, our single shallow system achieves BLEU=38. 8, comparable with the state-of-the-art deep models.

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IJCAI Conference 2016 Conference Paper

Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation

  • Yong Cheng
  • Shiqi Shen
  • Zhongjun He
  • Wei He
  • Hua Wu
  • Maosong Sun
  • Yang Liu

The attentional mechanism has proven to be effective in improving end-to-end neural machine translation. However, due to the intricate structural divergence between natural languages, unidirectional attention-based models might only capture partial aspects of attentional regularities. We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation. Instead of training source-to-target and target-to-source translation models independently, our approach encourages the two complementary models to agree on word alignment matrices on the same training data. Experiments on Chinese-English and English-French translation tasks show that agreement-based joint training significantly improves both alignment and translation quality over independent training.

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AAAI Conference 2016 Conference Paper

Improved Neural Machine Translation with SMT Features

  • Wei He
  • Zhongjun He
  • Hua Wu
  • Haifeng Wang

Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in fluent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method significantly improves the translation quality of the state-of-the-art NMT system on Chinese-to- English translation tasks. Our method produces a gain of up to 2. 33 BLEU score on NIST open test sets.

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TIST Journal 2011 Journal Article

Two-Word Collocation Extraction Using Monolingual Word Alignment Method

  • Zhanyi Liu
  • Haifeng Wang
  • Hua Wu
  • Sheng Li

Statistical bilingual word alignment has been well studied in the field of machine translation. This article adapts the bilingual word alignment algorithm into a monolingual scenario to extract collocations from monolingual corpus, based on the fact that the words in a collocation tend to co-occur in similar contexts as in bilingual word alignment. First, the monolingual corpus is replicated to generate a parallel corpus, in which each sentence pair consists of two identical sentences. Next, the monolingual word alignment algorithm is employed to align potentially collocated words. Finally, the aligned word pairs are ranked according to the alignment scores and candidates with higher scores are extracted as collocations. We conducted experiments on Chinese and English corpora respectively. Compared to previous approaches that use association measures to extract collocations from co-occurrence word pairs within a given window, our method achieves higher precision and recall. According to human evaluation, our method achieves precisions of 62% on a Chinese corpus and 64% on an English corpus. In particular, we can extract collocations with longer spans, achieving a higher precision of 83% on the long-span (> 6 words) Chinese collocations.

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