Traditional Conditional Random Fields Research Articles

A Survey of CRF Algorithm Based Knowledge Extraction of Elementary Mathematics in Chinese

Chinese word segmentation is an important research direction in related research on elementary mathematics knowledge extraction. The speed of segmentation directly affects subsequent applications, and the accuracy of segmentation directly affects corresponding research in the next step. In the machine learning methods for extracting basic mathematical knowledge points, the Conditional Random Field (CRF) model implements new word discovery well, and is increasingly used in knowledge extraction of basic mathematics. This article first introduces the traditional CRF process of named entity recognition. Then, an improved algorithm CRF++for conditional field model is proposed. Since the recognition rate of named entities based on traditional machine learning methods is not high, a post-processing method for entity recognition that automatically generates a dictionary is proposed. After identifying mathematical entities, a pruning strategy combining Viterbi algorithm and rules is proposed to achieve a higher recognition rate of elementary mathematical entities. Finally, several methods of disambiguation after entity recognition are introduced.

A Parallel Conditional Random Fields Model Based on Spark Computing Environment

As one of the famous probabilistic graph models in machine learning, the conditional random fields (CRFs) can merge different types of features, and encode known relationships between observations and construct consistent interpretations, which have been widely applied in many areas of the Natural Language Processing (NLP). With the high-speed development of the internet and information systems, some performance issues are certain to arise when the traditional CRFs deals with such massive data. This paper proposes SCRFs, which is a parallel optimization of CRFs based on the Resilient Distributed Datasets (RDD) in the Spark computing framework. SCRFs optimizes the traditional CRFs from these stages: First, with all features are generated in parallel, the intermediate data which will be used frequently are all cached into the memory to speed up the iteration efficiency. By removing the low-frequency features of the model, SCRFs can also prevent the overfitting of the model to improve the prediction effect. Second, some specific features are dynamically added in parallel to correct the model in the training process. And for implementing the efficient prediction, a max-sum algorithm is proposed to infer the most likely state sequence by extending the belief propagation algorithm. Finally, we implement SCRFs base on the version of Spark 1.6.0, and evaluate its performance using two widely used benchmarks: Named Entity Recognition and Chinese Word Segmentation. Compared with the traditional CRFs models running on the Hadoop and Spark platforms respectively, the experimental results illustrate that SCRFs has obvious advantages in terms of the model accuracy and the iteration performance.

Efficient robust conditional random fields.

Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.

Extracting Web Entity Activities Based on SVM and Extended Conditional Random Fields

PDF HTML阅读 XML下载 导出引用 引用提醒 基于SVM和扩展条件随机场的Web实体活动抽取 DOI: 10.3724/SP.J.1001.2012.04189 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(61003051); 国家科技支撑计划(2009BAH44B02); 山东省自然科学基金(2009ZRB019RW); 山东省科技攻关计划(2010GGX10108) Extracting Web Entity Activities Based on SVM and Extended Conditional Random Fields Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在传统信息抽取的基础上,研究 Web 实体活动抽取,基于格语法对实体活动进行了形式化定义,并提出一种基于SVM(supported vector machine)和扩展条件随机场的Web实体活动抽取方法,能够从Web上准确地抽取实体的活动信息.首先,为了避免人工标注训练数据的繁重工作,提出一种基于启发式规则的训练数据生成算法,将语义角色标注的训练数据集转化为适合 Web 实体活动抽取的训练数据集,分别训练支持向量机分类器和扩展条件随机场.在抽取过程中,通过分类器获得包含实体活动的语句,然后利用扩展条件随机场对传统条件随机场中不能利用的标签频率特征和关系特征建模,标注自然语句中的待抽取信息,提高标注的准确率.通过多领域的实验,其结果表明,所提出的抽取方法能够较好地适用于 Web 实体活动抽取. Abstract:On the basis of the traditional methods extracting information, this paper defines the formal model ofentity activity based on case grammar and presents a method based on supported vector machine and extendedcondition random fields to extract Web entity activities accurately. First, in order to automatically train the machinelearning models, the study puts forward a heuristic method to transform the semantic role labeling training data intothe training data of entity activity extraction. Next, the study trains a support vector machine classifier and extendscondition random fields using the training data. Third, using the classifier, the study distinguishes the sentences thatcontain Web entity activities. The paper also proposes forward and extends condition random fields to model thefrequency and relationship feature. The traditional conditional random fields cannot model this while the new modelcan label the entity activity information in natural language sentences more accurately. Finally, the experimentalresults show that the method is effective in multidomains and can be applied to Web entity activity extraction. 参考文献 相似文献 引证文献

Embedding Topic Discovery in Conditional Random Fields Model for Segmenting Nuclei Using Multispectral Data

Segmentation of cells/nuclei is a challenging problem in 2-D histological and cytological images. Although a large number of algorithms have been proposed, newer efforts continue to be devoted to investigate robust models that could have high level of adaptability with regard to considerable amount of image variability. In this paper, we propose a multiclassification conditional random fields (CRFs) model using a combination of low-level cues (bottom-up) and high-level contextual information (top-down) for separating nuclei from the background. In our approach, the contextual information is extracted by an unsupervised topic discovery process, which efficiently helps to suppress segmentation errors caused by intensity inhomogeneity and variable chromatin texture. In addition, we propose a multilayer CRF, an extension of the traditional single-layer CRF, to handle high-dimensional dataset obtained through spectral microscopy, which provides combined benefits of spectroscopy and imaging microscopy, resulting in the ability to acquire spectral images of microscopic specimen. The approach is evaluated with color images, as well as spectral images. The overall accuracy of the proposed segmentation algorithm reaches 95% when applying multilayer CRF model to the spectral microscopy dataset. Experiments also show that our method outperforms seeded watershed, a widely used algorithm for cell segmentation.

Region Contextual Visual Words for scene categorization

This paper proposes a method for scene categorization by integrating region contextual information into the popular Bag-of-Visual-Words approach. The Bag-of-Visual-Words approach describes an image as a bag of discrete visual words, where the frequency distributions of these words are used for image categorization. However, the traditional visual words suffer from the problem when faced these patches with similar appearances but distinct semantic concepts. The drawback stems from the independently construction each visual word. This paper introduces Region-Conditional Random Fields model to learn each visual word depending on the rest of the visual words in the same region. Comparison with the traditional Conditional Random Fields model, there are two areas of novelty. First, the initial label of each patch is automatically defined based on its visual feature rather than manually labeling with semantic labels. Furthermore, the novel potential function is built under the region contextual constraint. The experimental results on the three well-known datasets show that Region Contextual Visual Words indeed improves categorization performance compared to traditional visual words.

Image Binarization based on Conditional Random Fields

In recent years, Conditional Random Fields (CRF) are proposed and proved greatly useful in natural language processing, voice recognition and computer vision. In this paper we propose a variant of CRF to solve the problem of image binarization. Unlike previous image binariztion approaches, the Patch Random Fields (PRF) proposed here could provide global optimal solutions considering both the local information from source images and pixel-wise smoothness. In this new framework, we take image patch as a kind of raw information carrier and model it with mixture of probabilistic PCA. Moreover, traditional CRF always confronts difficulties in obtaining proper parameters for the probabilistic models; this process is often time-consuming and intractable. To mitigate this problem, we train most parameters in a generative way, and then optimize the remaining parameters using a gradient descent method. The advantages of generative models and CRF are thus well combined. Experimental results demonstrate our method's effectiveness.