Exploiting CCG Structures with Tree Kernels for Speculation Detection
نویسندگان
چکیده
Our CoNLL-2010 speculative sentence detector disambiguates putative keywords based on the following considerations: a speculative keyword may be composed of one or more word tokens; a speculative sentence may have one or more speculative keywords; and if a sentence contains at least one real speculative keyword, it is deemed speculative. A tree kernel classifier is used to assess whether a potential speculative keyword conveys speculation. We exploit information implicit in tree structures. For prediction efficiency, only a segment of the whole tree around a speculation keyword is considered, along with morphological features inside the segment and information about the containing document. A maximum entropy classifier is used for sentences not covered by the tree kernel classifier. Experiments on the Wikipedia data set show that our system achieves 0.55 F-measure (in-domain).
منابع مشابه
Semantic Kernels for Semantic Parsing
We present an empirical study on the use of semantic information for Concept Segmentation and Labeling (CSL), which is an important step for semantic parsing. We represent the alternative analyses output by a state-of-the-art CSL parser with tree structures, which we rerank with a classifier trained on two types of semantic tree kernels: one processing structures built with words, concepts and ...
متن کاملConvolution Kernels for Subjectivity Detection
In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without deriving explicit features. The feature design for the detection of subjective expressions is fairly difficult and there currently exists no commonly ...
متن کاملStructured Lexical Similarity via Convolution Kernels on Dependency Trees
A central topic in natural language processing is the design of lexical and syntactic features suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical similarities. We define efficient and powerful kernels for measuring the similarity between dependency structures, whose surf...
متن کاملCCG-Based RNA Secondary Structure Prediction
Various systems have been proposed to predict secondary structures of RNAs using their sequence information. Among them, Uemura et al. [2] described a system that recognizes some typical RNA secondary structures such as hairpin loops and pseudoknots with Tree Adjoining Grammar. However, their work captures only known sub-structures, and not those unknown sub-structures that might also exist. Te...
متن کاملA* CCG Parsing with a Supertag and Dependency Factored Model
We propose a new A* CCG parsing model in which the probability of a tree is decomposed into factors of CCG categories and its syntactic dependencies both defined on bi-directional LSTMs. Our factored model allows the precomputation of all probabilities and runs very efficiently, while modeling sentence structures explicitly via dependencies. Our model achieves the stateof-the-art results on Eng...
متن کامل