Convolution tree kernels are an efficient and effective method for comparing syntactic structures in NLP methods.

However, current kernel methods such as subset tree kernel and partial tree kernel understate the similarity of very similar tree structures.

2.1 Syntax-based Tree Kernels

Syntax-based tree kernels quantify the similarity between two constituent parses by counting their common substructures.

The partial tree kernel (PTK) relaxes the definition of subtrees to allow partial production rule

Convolution kernels over syntactic trees ( tree kernels ) offer a potential solution to this problem by providing relatively efficient algorithms for computing similarities between entire discrete structures.

However, conventional tree kernels are sensitive to pattern variations.

For example, two trees in Figure 1(a) sharing the same structure except for one terminal symbol are deemed at most 67% similar by the conventional tree kernel (PTK) (Moschitti, 2006).

Compared with the previous tree kernels , our descending path kernel has the following advantages: l) the substructures are simplified so that they are more likely to be shared among trees, and therefore the sparse feature issues of previous kernels could be alleviated by this representation; 2) soft matching between two similar structures (e.g., NP—>DT JJ NN versus NP—>DT NN) have high similarity without reference to any corpus or grammar rules;

Tree kernel is an effective technique for relation extraction.

In this paper, we propose a new tree kernel, called feature-enriched tree kernel (F TK ), which can enhance the traditional tree kernel by: 1) refining the syntactic tree representation by annotating each tree node with a set of discriminant features; and 2) proposing a new tree kernel which can better measure the syntactic tree similarity by taking all features into consideration.

Experimental results show that our method can achieve a 5.4% F—measure improvement over the traditional convolution tree kernel .

An effective technique is the tree kernel (Zelenko et al., 2003; Zhou et al., 2007; Zhang et al., 2006; Qian et al., 2008), which can exploit syntactic parse tree information for relation extraction.

Then the similarity between two trees are computed using a tree kernel, e. g., the convolution tree kernel proposed by Collins and Duffy (2001).

Unfortunately, one main shortcoming of the traditional tree kernel is that the syntactic tree representation usually cannot accurately capture the

We rely on the tree kernel technology to automatically extract and learn features with better generalization power than bag-of-words.

In particular, we define an efficient tree kernel derived from the Partial Tree Kernel , (Moschitti, 2006a), suitable for encoding structural representation of comments into Support Vector Machines (SVMs).

These trees are input to tree kernel functions for generating structural features.

The latter are automatically generated and learned by SVMs with expressive tree kernels .

In other words, the tree fragment: [S [negative—VP [negative—V [destroy] ] [PRODUCT-NP [PRODUCT-N [xoom] ] ] ] is a strong feature (induced by tree kernels ) to help the classifier to discriminate such hard cases.

the domain context tree kernel contributed to produce more precise outputs.

Our composite kernel consists of a history sequence and a domain context tree kernels , both of which are composed based on similar textual units in Wikipedia articles to a given dialog context.

Our composite kernel consists of two different kernels: a history sequence kernel and a domain context tree kernel .

3.2 Domain Context Tree Kernel

Since this constructed tree structure represents semantic, discourse, and structural information extracted from the similar Wikipedia paragraphs to each given instance, we can explore these more enriched features to build the topic tracking model using a subset tree kernel (Collins and Duffy, 2002) which computes the similarity between each pair of trees in the feature space as follows:

Many of them focus on using tree kernels to learn parse tree structure related features (Collins and Duffy, 2001; Culotta and Sorensen, 2004; Bunescu and Mooney, 2005).

For example, by combining tree kernels and convolution string kernels, (Zhang et al., 2006) achieved the state of the art performance on ACE data (ACE, 2004).

We compare our approaches to three state-of-the-art approaches including SVM with convolution tree kernels (Collins and Duffy, 2001), linear regression and SVM with linear kernels (Scholkopf and Smola, 2002).

To adapt the tree kernel to medical domain, we followed the approach in (Nguyen et al., 2009) to take the syntactic structures into consideration.

We also added the argument types as features to the tree kernel .

In this field, the tree kernel based method commonly uses the parse tree to capture the structure information (Zelenko et al., 2003; Culotta and Sorensen, 2004).

(2012) proposed a convolution tree kernel .

(2010) employed a model, combining both the feature based and the tree kernel based methods.

(2008; 2010) also pointed out that, due to the inaccuracy of Chinese word segmentation and parsing, the tree kernel based approach is inappropriate for Chinese relation extraction.

2.2 Tree kernels

Tree Kernel methods have gained popularity in the last decade for capturing syntactic information in the structure of parse trees (Collins and Duffy, 2002; Moschitti, 2006).

(2013) have attempted to provide formulations to incorporate semantics into tree kernels through the use of distributional word vectors at the individual word-nodes.

A number of metrics have been proposed to measure the similarity between two labeled trees, e. g., Tree Edit Distance (Tai, 1979) and Tree Kernels (Collins and Duffy, 2001; Moschitti and Basili, 2006).

Tree kernels (TKs) provide an effective way to integrate arbitrary tree structures in kernel-based machine learning algorithms like SVMs.

the nuclearity and the relations, in order to allow the tree kernel to give partial credit to subtrees that differ in labels but match in their skeletons.