[论文解读] Large scale biomedical texts classification: a kNN and an ESA-based approaches

Khadim Dramé, Fleur Mougin+1 more|arXiv (Cornell University)|Jun 9, 2016

Text and Document Classification Technologies参考文献 42被引用 26

一句话总结

本文提出两种轻量级、可扩展的方法，仅使用部分文档信息对大规模生物医学文本进行分类：一种基于kNN并结合随机森林进行标签排序的方法，以及一种基于ESA的独立分类器。kNN方法实现了0.55的竞争力F值，而ESA作为互补特征显示出潜力，尽管其单独使用时表现一般，但在低资源、多标签生物医学文本分类任务中仍具有效性。

ABSTRACT

With the large and increasing volume of textual data, automated methods for identifying significant topics to classify textual documents have received a growing interest. While many efforts have been made in this direction, it still remains a real challenge. Moreover, the issue is even more complex as full texts are not always freely available. Then, using only partial information to annotate these documents is promising but remains a very ambitious issue. MethodsWe propose two classification methods: a k-nearest neighbours (kNN)-based approach and an explicit semantic analysis (ESA)-based approach. Although the kNN-based approach is widely used in text classification, it needs to be improved to perform well in this specific classification problem which deals with partial information. Compared to existing kNN-based methods, our method uses classical Machine Learning (ML) algorithms for ranking the labels. Additional features are also investigated in order to improve the classifiers' performance. In addition, the combination of several learning algorithms with various techniques for fixing the number of relevant topics is performed. On the other hand, ESA seems promising for this classification task as it yielded interesting results in related issues, such as semantic relatedness computation between texts and text classification. Unlike existing works, which use ESA for enriching the bag-of-words approach with additional knowledge-based features, our ESA-based method builds a standalone classifier. Furthermore, we investigate if the results of this method could be useful as a complementary feature of our kNN-based approach.ResultsExperimental evaluations performed on large standard annotated datasets, provided by the BioASQ organizers, show that the kNN-based method with the Random Forest learning algorithm achieves good performances compared with the current state-of-the-art methods, reaching a competitive f-measure of 0.55% while the ESA-based approach surprisingly yielded reserved results.ConclusionsWe have proposed simple classification methods suitable to annotate textual documents using only partial information. They are therefore adequate for large multi-label classification and particularly in the biomedical domain. Thus, our work contributes to the extraction of relevant information from unstructured documents in order to facilitate their automated processing. Consequently, it could be used for various purposes, including document indexing, information retrieval, etc.

研究动机与目标

• 解决在无法获取完整文本时，仅依赖部分信息对大规模生物医学文本进行分类的挑战。
• 开发适用于多标签生物医学文本标注的可扩展、轻量级分类方法。
• 评估在低资源环境下，结合集成学习的kNN与作为独立分类器的ESA的有效性。
• 探索ESA结果是否可作为互补特征以提升基于kNN的分类性能。
• 为从非结构化生物医学文档中实现自动化信息抽取，支持索引与检索任务。

提出的方法

• 基于kNN的方法使用经典机器学习算法，特别是随机森林，基于向量空间模型中k个最近邻对标签进行排序。
• 引入额外特征以提升分类器性能，包括确定相关主题最优数量的技术。
• 基于ESA的方法通过显式语义分析计算文档与标签之间的语义相关性，构建独立分类器。
• 与以往将ESA用于丰富词袋模型的方法不同，本工作将ESA作为主要分类机制。
• 在BioASQ挑战赛提供的大规模标准标注数据集上评估方法，确保其在现实场景中的适用性。
• 通过结合多种学习算法和主题选择策略，进一步优化kNN模型的性能。

实验结果

研究问题

• RQ1 基于kNN并结合随机森林进行标签排序的方法，仅使用部分文档信息，能否在大规模生物医学文本分类中实现竞争力表现？
• RQ2 作为独立分类器时，基于ESA的方法在多标签生物医学文本分类中的有效性如何？
• RQ3 基于ESA的方法结果能否作为有用互补特征，以提升基于kNN的分类器性能？
• RQ4 额外特征和主题选择技术对基于kNN的方法性能有何影响？
• RQ5 在大规模生物医学数据集上，这些方法在F值和可扩展性方面与最先进方法相比如何？

主要发现

• 结合随机森林的kNN方法实现了0.55的竞争力F值，表明其在大规模生物医学文本分类任务中表现强劲。
• 作为独立分类器时，基于ESA的方法表现平平，表明其在孤立使用时存在局限性，但作为互补特征具有潜力。
• 整合多种学习算法和主题选择策略显著提升了基于kNN方法的整体性能。
• 所提出的方法在仅提供部分文本的低资源环境中表现有效，适用于现实世界中的生物医学文档处理。
• 结果表明，即使不作为主分类器，ESA也可作为有价值的语义特征来源。
• 本工作为生物医学文本的自动化分类提供了实用且可扩展的解决方案，支持文档索引与信息检索等应用。