摘要
随着监督式机器学习技术在各个领域的广泛应用,研究人员逐渐意识到,训练数据的缺乏是阻碍学习模型快速部署的关键因素之一。最近几年,如何解决训练数据缺乏的问题,已经成为机器学习,自然语言处理,信息检索、多媒体等领域的研究热点。
排序学习是信息检索的关键问题之一。目前,基于监督的机器学习技术被认为是解决排序学习的最佳选择。如同传统监督式学习技术,目标领域缺乏训练数据也是排序学习正在面临的实际问题。针对排序学习,我们研究了如何利用其他相关领域的己有训练数据,学习出适用于目标领域的模型,即领域自适应。
本文的主要贡献有以下几个方面：
1.提出了基于文档权重的排序学习自适应框架。首先,利用领域分隔超平面估计源领域文档对目标领域的重要性；然后,把这些文档权重转换成文档对权重；最终,文档对权重可以集成到基于文档对的排序学习算法中。
2.研究了著名的排序学习算法RankBoost的领域自适应问题。在基于文档权重的排序自适应框架下,提出了三种基于文档权重的RankBoost算法,并分别对它们进行了理论分析和试验比较。
3.提出了直接在查询层次估计源领域查询对目标领域重要性的方法。在排序学习中,查询是带有相关性标签的文档集合,是排序学习的基本对象。我们分别从两个不同的角度进行查询权重估计：(1)将查询压缩成特征向量,然后采用传统的权重估计方法进行查询权重估计。(2)对每个源领域的查询,把它依次和目标领域查询进行比较；通过集成这些两两比较的结果,估计源领域查询对于目标领域的重要程度。
4.提出了基于主动学习的排序学习自适应算法。为了获得目标领域特有的排序知识,采用主动学习技术,选择少量目标领域具有信息量的查询进行标注。这些查询可以弥补源领域所缺失的目标领域排序知识,同时,利用这些目标查询评估源查询对目标领域的重要性权重,从而充分利用源领域的训练数据。
5.将领域自适应技术应用在语义实体识别中,提出了利用领域独立特征来增强领域自适应能力的方法。传统实体识别仅仅使用短文本特征,当训练文档和测试文档在风格上稍有差异时,性能便有明显下降。为解决该问题,我们设计了一个CRF与SVM的组合框架,通过该框架,短文本特征与领域独立特征可以有效的集成,最终获得的识别模型能够更好地适用于目标领域。
针对不同的应用场景,本文研究了排序学习中的领域自适应问题。在目标领域没有标注数据的场景下,我们从样本权重的角度,研究了基于权重的排序学习领域自适应；在目标领域存在少量标注预算的场景下,我们研究了基于主动学习的排序自适应算法。另外,我们还研究了领域自适应在语义实体识别中的应用。从特征的角度,我们探讨了基于领域独立特征的语义实体识别领域自适应。我们在标准数据集上评价了算法的有效性。
在实际应用中,比如多媒体新闻推荐,热点事件检测,情感分析,通用搜索,垂直搜索等等,通过本文提出的领域自适应技术,可以充分利用已有其他相关领域的标注数据,降低目标领域的标注成本,同时获得满意的目标领域模型。
With the widespread use of supervised machine learning techniques in many fields, researchers have considered that the scarce of training data in target domain becomes one of the important reasons which prevent us from quickly deploying a learned model. In re-cent years, how to resolve this problem has became a hot topic in several communities such as machine learning, natural language processing, information retrieval and multimedia.
Learning to rank is one of the key problems in information retrieval. To date the techniques based on supervised learning have been regarded as the best choice for learning to rank. However, like traditional supervised learning, we also need to resolve the similar problem in ranking learning, i.e. the lack of training data in target domain. For this purpose, we have investigated on how to effectively use labeled data in related domains to learn a model for target domain, which is referred as domain adaptation.
The contributions of this paper include:
1. We proposed a framework based on document weighting for learning to rank.. First, we estimate the source document's importance to target domain by domain separator; Then, the weight of document can be transformed to the weight of document pair, which may be integrated into pairwise ranking algorithms.
2. We investigated the adaptation problem of RankBoost, a famous ranking algorithm. In the framework of ranking adaptation based on document weighting, we proposed three versions of weight-based RankBoost, and made theoretical analysis and empir-ical comparisons.
3. We proposed to estimate the source query's importance to target domain at query level. In learning to rank, the learned object is a query which contains a set of retrieved documents with relevance label. We estimate the query importance from two distinct perspectives:(1) The query can be compressed into a feature vector, and then we use traditional approaches to estimate the query importance. (2) For each source query, we measure the similarity between the source query and each target query, and then combine these fine-grained similarity values to estimate its importance.
4. We proposed an algorithm of domain adaptation based on active learning. For obtain-ing target domain-specific ranking knowledge, we adopt active learning techniques to select a small number of informative target queries to label. These queries can provide the domain-specific knowledge which not contained in source domain. Simultaneously, we use these target queries to evaluate the importance weight of source queries such that the source training data can be reused.
5. We applied the technique of domain adaptation to semantic entity detection, and proposed to improve the adaptation capability using domain independent features. Traditionally, only short context features are used in entity detection. The perfor-mance is degraded when the genre of test documents is different from that of training documents. To resolve this problem, we designed the framework combining CRF and SVM. The framework can effectively integrate short context and domain independent features, and thus the learned model can adapt well to target domain.
In this thesis, we studied the domain adaptation problem in learning to rank under dif-ferent scenarios. When labeled data are unavailable in target domain, we investigated the weight-based adaptation for learning to rank from the instance weighting point of view. When target domain is allocated limited budget, we explored the ranking adap-tation based on active learning technique. Additionally, we also studied the application of domain adaptation to semantic entity detection. From the perspective on feature, we explored the adaptation of semantic entity detection using domain independent features. The effectiveness of all proposed algorithms are evaluated on several benchmark dataset.
In real applications such as multimedia news recommendation, hot event detection, sentiment analysis, web search, vertical search and so on, with the proposed domain adaption algorithms, existing labeled data in related domain can be effectively used to learn a model for target domain, such that the label cost may be saved.
引文
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