树核驱动的语义关系提取：融合语法与语义信息的新方法

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"本文介绍了一种利用丰富语法和语义信息的树核方法，用于从文本中提取命名实体之间的语义关系。通过构建一个包含语法和语义信息的丰富语义关系树结构，该方法能够更有效地识别和理解实体间的关联。接着，文章提出了一个上下文敏感的卷积树内核，这种内核可以捕捉到树结构中的结构信息，通过考虑子树的祖先节点路径作为上下文。实验结果显示，这种方法在自动内容提取/关系检测和表征（ACE RDC）语料库上的性能优于现有的其他最新技术。" 在语义关系提取任务中，通常需要识别和理解文本中不同实体之间的联系，如人物的关系、事件的发生等。传统的基于规则或统计的方法可能无法充分利用语境和语法结构，从而影响提取效果。针对这一问题，本文引入了“丰富语义关系树结构”这一概念。这个结构通过解析树与实体对结合，整合了句法和语义两方面的信息，使得关系的表示更加完整和深入。树核方法是自然语言处理中的一个重要工具，它允许对树形结构进行相似度计算。本文提出的“上下文敏感的卷积树内核”是对传统树内核的扩展。传统树内核主要比较树结构的局部子树，而上下文敏感的版本则引入了上下文的概念，即通过考虑子树的祖先节点路径，能够捕获到更丰富的语境信息。这种改进有助于模型理解实体关系时的复杂性，尤其是在语义关系的判断上，考虑到上下文可以提高准确性和鲁棒性。实验部分，研究者使用了自动内容提取/关系检测和表征（ACE RDC）语料库进行验证。这是一个广泛使用的标准数据集，包含各种实体类型和关系。结果证明，该方法在处理语义关系提取时，相比于其他最新技术，表现出了优越的性能，这表明其在实际应用中具有很高的潜力。本文的工作为语义关系提取提供了一个新颖且有效的框架，通过整合语法和语义信息，并利用上下文敏感的卷积树内核，提高了关系提取的准确性和效率。这对于自然语言处理领域的进步，特别是在信息抽取、问答系统以及知识图谱构建等方面，具有重要的理论和实践价值。

automatically determined to better include syntactic and semantic information. Moreover, a context-sensitive CTK,

which enumerates both context-free and context-sensitive sub-trees by considering their ancestor node paths as their

contexts, is proposed to better capture structural information in the semantic relation tree structure. Finally, our tree

kernel and a state-of-the-art linear kernel are interpolated by using a composite kernel to evaluate their complementary

nature.

The layout of this paper is as follows: First, related work is reviewed in more detail in Section 2. The rich semantic relation

tree structure is then introduced in Section 3, while the context-sensitive CTK is proposed in Section 4. In Section 5, the tree

kernel-based semantic relation extraction is systematically evaluated on the ACE RDC corpora. Finally, our work is concluded

in Section 6.

2. Related work

Semantic relation extraction was ﬁrst introduced as part of the Template Element task in the sixth Message Understand-

ing Conference (MUC-6) and then formulated as the template relation task in the seventh Message Understanding Confer-

ence (MUC-7). With the introduction of the ACE program, it was further reformulated as the RDC task in the ACE program.

Since then, many methods, such as feature vector-based methods [8,10,25–28], tree kernel-based methods [20,6,2,21,22],

and composite kernel-based methods [24,21,22] have been proposed in the literature.

For the feature vector-based methods, Kambhatla [10] employed Maximum Entropy models to combine diverse lexical,

syntactic and semantic features in semantic relation extraction, and achieved an F-measure of 52.8 on the 24 relation sub-

types of the ACE RDC 2003 corpus. Zhou et al. [25,27] systematically explored diverse features through a linear kernel and

with Support Vector Machines (SVM), and achieved F-measures of 68.0 and 55.5 on the ﬁve relation types and the 24 relation

subtypes of the ACE RDC 2003 corpus, respectively. Zhou et al. [26,28] further improved the performance by exploring the

commonality among related classes in a class hierarchy with a hierarchical learning strategy. Jiang and Zhai [8] also system-

atically evaluated the effectiveness of different feature subspaces with different complexities and obtained the best F-mea-

sure of 71.5 on the seven relation types of the ACE RDC 2004 corpus. One problem with feature vector-based methods is that

they often require extensive feature engineering (e.g. feature design, implementation and selection). Another problem is that

although they can explore some structural information in the parse tree, it is difﬁcult to preserve the structural information

in the parse trees with the feature vector-based methods (e.g., [10] used the non-terminal path connecting the two given

entities in a parse tree, while Zhou et al. [23,27] introduced additional chunking features to enhance the performance).

As an alternative to the feature vector-based methods, the kernel-based methods [7] have been proposed to implicitly

explore various features in a high dimensional space by employing a kernel to directly calculate the similarity between

two objects. In particular, kernel-based methods can be effective in reducing the burden of feature engineering for structured

objects in Natural Language Processing (NLP) research, such as the tree structure in semantic relation extraction.

Zelenko et al. [20] proposed a kernel between two parse trees, which recursively matches nodes from roots to leaves in a

top-down manner. For each pair of matched nodes, a subsequent kernel on their child nodes is invoked. They achieved great

success in two simple semantic relation extraction tasks. Culotta and Sorensen [6] extended their work to estimate the sim-

ilarity between augmented dependency trees and achieved an F-measure of 45.8 on the ﬁve relation types of the ACE RDC

2003 corpus. One problem with the above two tree kernels is that two matched nodes must be at the same height and have

the same path to their respective root nodes. Bunescu and Mooney [2] proposed the shortest path dependency tree kernel,

which sums up the number of common word classes at each position in the two paths, and achieved an F-measure of 52.5 on

the ﬁve relation types of the ACE RDC 2003 corpus. They argued that the information to model a relationship between two

entities could be typically captured by the shortest path between them in the dependency graph. Their kernel is unable to

fully preserve the structured dependency tree information, and it is also conditioned by the fact that the two matched paths

should have the same length. This makes it suffer from behavior similar to that reported in the work of Culotta and Sorensen

[6], that is, high precision but low recall.

To develop an effective tree kernel method, Zhang et al. [21,22] explored various semantic relation tree structures and

used the standard CTK over semantic relation trees [5] to model structural information for semantic relation extraction.

They achieved F-measures of 61.9 and 63.6 on the ﬁve relation types of the ACE RDC 2003 corpus and the seven relation

types of the ACE RDC 2004 corpus, respectively, without entity-related information, while the F-measure on the ﬁve rela-

tion types of the ACE RDC 2003 corpus reached 68.7 when entity-related information was included in the parse tree. One

problem

with

the standard CTK is that the sub-trees involved in the tree kernel computation are context-free, that is, they

do not consider the information outside the sub-trees. This is different from the tree kernel in [6], where the sub-trees

involved in the tree kernel computation are context-sensitive (i.e., they consider the path from the tree root node to

the sub-tree root node). Zhang et al. [21,22] also showed that the widely-used SPT structure performed best. However,

one problem with the SPT is that it fails to capture the contextual information outside the shortest path, yet such infor-

mation is important for semantic relation extraction in many cases. Our random selection of 100 positive training in-

stances from the ACE RDC 2003 training corpus shows that about 25% of the cases need contextual information outside

the shortest path. Among others, Bunescu and Mooney [3] proposed a subsequence kernel and applied it in protein–pro-

tein interaction extraction and the ACE RDC tasks. Zhang et al. [23] employed a grammar-driven CTK in semantic role

labeling and achieved certain success, following the work pioneered by Moschitti [13].

G. Zhou et al. / Information Sciences 180 (2010) 1313–1325

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