反馈日志与混合概率模型相结合的图像标注

为提高图像标注质量,提出一种反馈日志与混合概率模型相结合的图像标注方法。利用本体语义网计算标注词之间的相似性度,将相似度应用于日志分析,得到具体应用中的标注词间关系,结合标注词间的关系和图像底层特征,使用混合概率模型进行自动图像标注。实验结果表明,该方法能获得较好的查全率和查准率。     

图像自动语义标注技术综述

近年来,随着对基于内容图像检索技术研究的深入,图像自动语义标注已成为了该领域的研究热点。针对目前广泛研究的图像语义标注技术,从其分类、关键技术、存在问题及发展方向进行了进行了论述,以期为从事该方向研究的人员提供一定的借鉴意义和参考价值。     

A Machine Learning Approach for Identification Thesis and Conclusion Statements in Student Essays

This study describes and evaluates twoessay-based discourse analysis systems thatidentify thesis and conclusion statements fromstudent essays written on six different essaytopics. Essays used to train and evaluate thesystems were annotated by two human judges,according to a discourse annotation protocol. Using a machine learning approach, a number ofdiscourse-related features were automaticallyextracted from a set of annotated trainingdata. Using these features, two discourseanalysis models were built using C5.0 withboosting: a topic-dependent and atopic-independent model. Both systemsoutperformed a positional algorithm. While thetopic-dependent system showed somewhat higherperformance, the topic-independent systemshowed similar results, indicating that asystem can generalize to unseen data – thatis, essay responses on topics that the systemhas not seen in training.     

基于极端梯度提升树算法的图像属性标注

提出基于极端梯度提升树(eXtreme gradient boosting,XGBoost)算法的图像属性标注模型,以改善标注性能：提取图像局部二值模式(local binary patterns,LBP)、灰度纹理空间包络特征(Gist)、尺度不变特征变换(scale invariant feature transform,SIFT)、视觉几何组(visual geometry group,VGG)等特征,以准确刻画图像视觉内容;基于图像特征,采用XGBoost算法集成弱分类器为强分类器,完成图像属性标注;深入挖掘图像属性蕴含的深层语义,构建全新的、层次化的属性表示体系,以贴近人类客观认知;设计迁移学习策略并合理组合分类模型,进一步改善标注性能。试验表明：Gist特征能真实刻画图像视觉内容;执行基础迁移学习后,标注精准度比迁移学习前最优指标提升8.69%;执行混合型迁移学习后,合理组合分类模型,标注精准度比基础迁移学习的最优指标提升17.55%。模型有效地改善图像属性标注精度。     

Improving Korean verb-verb morphological disambiguation using lexical knowledge from unambiguous unlabeled data and selective web counts

This paper deals with verb-verb morphological disambiguation of two different verbs that have the same inflected form. The verb-verb morphological ambiguity (VVMA) is one of the critical Korean parts of speech (POS) tagging issues. The recognition of verb base forms related to ambiguous words highly depends on the lexical information in their surrounding contexts and the domains they occur in. However, current probabilistic morpheme-based POS tagging systems cannot handle VVMA adequately since most of them have a limitation to reflect a broad context of word level, and they are trained on too small amount of labeled training data to represent sufficient lexical information required for VVMA disambiguation.In this study, we suggest a classifier based on a large pool of raw text that contains sufficient lexical information to handle the VVMA. The underlying idea is that we automatically generate the annotated training set applicable to the ambiguity problem such as VVMA resolution via unlabeled unambiguous instances which belong to the same class. This enables to label ambiguous instances with the knowledge that can be induced from unambiguous instances. Since the unambiguous instances have only one label, the automatic generation of their annotated corpus are possible with unlabeled data.In our problem, since all conjugations of irregular verbs do not lead to the spelling changes that cause the VVMA, a training data for the VVMA disambiguation are generated via the instances of unambiguous conjugations related to each possible verb base form of ambiguous words. This approach does not require an additional annotation process for an initial training data set or a selection process for good seeds to iteratively augment a labeling set which are important issues in bootstrapping methods using unlabeled data. Thus, this can be strength against previous related works using unlabeled data. Furthermore, a plenty of confident seeds that are unambiguous and can show enough coverage for learning process are assured as well.We also suggest a strategy to extend the context information incrementally with web counts only to selected test examples that are difficult to predict using the current classifier or that are highly different from the pre-trained data set.As a result, automatic data generation and knowledge acquisition from unlabeled text for the VVMA resolution improved the overall tagging accuracy (token-level) by 0.04%. In practice, 9-10% out of verb-related tagging errors are fixed by the VVMA resolution whose accuracy was about 98% by using the Naïve Bayes classifier coupled with selective web counts.     

基于语义分类的文物图像标注研究

由于图像数据中普遍存在的“语义鸿沟”问题，传统的基于内容的图像检索技术对于数字图书馆中的图像检索往往力不从心。而图像标注能有效地弥补语义的缺失。文中分析了图像语义标注的现状以及存在的问题，提出了基于语义分类的文物语义标注方法。算法首先通过构建一个Bayes语义分类器对待标注图像进行语义分类，进而通过在语义类内部建立基于统计的标注模型，实现了图像的语义标注。在针对文物图像进行标注的实验中，该方法获得了较好的标注准确率和效率。     

中文电子病历命名实体标注语料库构建

针对中文电子病历命名实体语料标注空白的现状,研究了中文电子病历命名实体标注语料库的构建。参考2010年美国国家集成生物与临床信息学研究中心(1282)给出的电子病历命名实体类型及修饰类型的定义,在专业医生的指导下制定了详尽的中文电子病历标注规范;通过对大量中文电子病历的分析,提出了一套完整的中文电子病历命名实体标注方案,而且采用预标注和正式标注的方法,建立了一定规模的中文电子病历命名实体标注语料库,其标注语料的一致性达到了92%以上。该工作对中文电子病历的命名实体识别及信息抽取研究提供了可靠的数据支持,对医疗知识挖掘也有重要意义。     

中文社区问答中问题答案质量评价和预测

知识共享型网站为自动问答系统带来了新的研究契机。但用户提供的问题及其答案质量参差不齐,在提供有用信息的同时可能包含各种无关甚至恶意的信息。对此类信息进行判别和过滤,并选取高质量的问题与答案对,有助于在基于社区的自动问答系统中重用相关问题的答案以提高问答系统的服务质量。首先从中文社区问答网站上抓取大量问题及答案,利用社会网络的方法对提问者和回答者的互动关系及特点进行了统计与分析。然后基于给定的问答质量判定标准,对3000多个问题及其答案进行了人工标注。并通过提取文本和非文本两类特征集,利用机器学习算法设计和实现了基于特征集的问答质量分类器。试验结果表明其精度和召回率均在70%以上。最后分析了影响社区网络中问答质量的主要因素。     

基于条件随机场的中医临床病历命名实体抽取

中医临床病历是中医重要的科研数据资源,但目前临床病历仍以文本为主要表达形式,对病历数据深入分析的前提是进行结构化信息抽取,而命名实体抽取是其基础性步骤。针对中医临床病历的命名实体,如症状、疾病和诱因等的抽取问题,通过手工标注的413份病历数据(以中文字为特征)与4类特征模版,将条件随机场(CRF)、隐马尔科夫模型(HMM)和最大熵马尔科夫模型(MEMM)用于中医病历命名实体抽取的实验,并进行比较分析。结果表明,结合合适的特征模版,CRF命名实体抽取方法取得了较好的性能,F1值的症状达到0.80,疾病名称达到0.74,诱因0.74。与HMM和MEMM相比,CRF有最高的准确率和召回率,是一种较为适用的中医临床病历命名实体抽取方法。