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Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards...
Question generation is an emerging research area of artificial intelligence in education. Question authoring tools are important in educational technologies, e.g., intelligent tutoring systems, as well as in dialogue systems. Approaches to generate factual questions, i.e., questions that have concrete answers, mainly make use of the syntactical and semantic information in a declarative sentence, which...
Shallow semantic parsing of natural language processing is an important component in all kind of NLP applications and Semantic Role Labeling in particular, is an active research topic. This paper describes a rule-based Semantic Role Labeling system aimed at extracting semantic information from texts. The input text is processed by exploiting part of speech information and syntactic dependencies in...
This paper presents the general architecture of a system which creates a map of semantic information around a named entity (Person, Organization, etc.). Thus, after the user specifies a named entity, the system searches on the web and returns the first 200 web pages containing the specified entity, applies semantic roles on the returned paragraphs, and extracts a map of related actions involving the...
To relieve "News Information Overload", classification, summarization and recommendation techniques have been proposed. However, these techniques fail to provide sufficient semantic information about news events. In this paper, considering5W1H (Who, What, Whom, When, Where and How), the full list of elements of a news article, we propose a novel approach to extract event semantic elements...
This paper reports our work on Chinese semantic role labeling, which takes advantage of hierarchical semantic knowledge from a common sense knowledge base named HowNet. On one hand, the words in lexical features such as predicate and head word are generalized with their hypernyms in HowNet. On the other hand, the hypernym-hyponym relation between sememes is used to capture the semantic similarity...
This paper presents a combination base machine learning approach to spatial semantic analysis in Chinese. The model consists of multiple pre-training classifiers and a gating mechanism for integrating the outputs of these classifiers. Then we use EM algorithm to train the parameters of the combining model. Finally the experimental results show an overall improvement on the standard corpus CPB.
In the paper, we choose Chinese propbank (CPB) as the experimental data to do the semantic role labeling task. In Chinese propbank, there are many sentences which contain multiple verbs, but how to deal with these sentences is seldom referred. In our experiment, we propose a method to deal with these sentences and add some new features. In order to get a feature, we take two-phase classification:...
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