Event causality is indispensable for knowledge-driven intelligent systems. In this paper, we propose a supervised method of extracting event causalities such as forest is cut down $$\rightarrow $$ forest is destroyed from web text. While relation identification using lexico-syntactic patterns (LSPs) is not novel, it is still challenging to extract the event expressions with necessary arguments from identified causality mentions. To address this issue, our method divides event-pair extraction into two phases: event boundary identification and missing argument identification. In the first phase, we propose a Naive Baysian probability method to identify the boundary of causal events, and extract the corresponding text fragments as event expressions. Secondly, we learn a multi-class decision tree (LADTree) to identify the missing argument for each incomplete event. Experimental results showed the good effectiveness of our approach on a large-scale open corpus.

In Chinese, there are many characters which are similar in shape, and this phenomenon usually induces writing errors. As one important issue in spelling automatic correction, shape similarity measurement is still a challenging problem. To address this issue, we propose a component-tree based method in this paper, which is based on the hypothesis “characters are similar if their construction and components are both similar”. Firstly, we decompose each character to a tree recursively, in which the root node is the character and the leaf nodes are atomic parts, called strokes. Then, we align any pair of trees using their minimal super-tree and calculate their similarity from bottom to up based on weighted edit distance. Finally, the cognitive prominence is used to adjust the similarity scores. In text proofreading experiments, our method achieved 97% precision and 95.6% recall, which can be applied in practical systems.

We propose the default description logic $$\mathcal {D}2\mathcal {L}$$ and the double-level default description logic $$\mathcal {D}3\mathcal {L}$$ . $$\mathcal {D}2\mathcal {L}$$ embeds normal defaults inside the basic description logic $$\mathcal {ALC},$$ and $$\mathcal {D}3\mathcal {L}$$ augments $$\mathcal {D}2\mathcal {L}$$ with normal double-level defaults. Double-level defaults are defaults of defaults and can be used to represent default inheritance of default properties of concepts in ontologies. A $$\mathcal {D}3\mathcal {L}$$ knowledge base ( $$\mathcal {D}3\mathcal {L}$$ -KB) can be divided into two levels of knowledge bases, and correspondingly its extensions can be computed in two steps. $$\mathcal {D}3\mathcal {L}$$ is more expressive than $$\mathcal {D}2\mathcal {L}$$ since there is a $$\mathcal {D}3\mathcal {L}$$ -KB that cannot reduce to any $$\mathcal {D}2\mathcal {L}$$ -KB. Specifically, there is a $$\mathcal {D}3\mathcal {L}$$ -KB such that the set of all its extensions cannot be exactly generated by any $$\mathcal {D}2\mathcal {L}$$ -KB.

The Chinese Framework of Semantic Taxonomy and Description (FSTD) is a linguistic resource that stores lexical and predicate-argument semantics about events or states in Chinese text, developed with the application of knowledge acquisition from Chinese text in mind. In this paper we build a web information extraction system, called NkiExtractor, to evaluate FSTD experimentally. We use two metrics: grammar coverage measures whether there is a semantic category of FSTD that corresponds to an event description in text, and extraction precision measures whether the correct predicate-argument structure can be extracted from text. Experimental results show that FSTD is a fairly comprehensive and effective resource for knowledge acquisition. We also discuss future work for expanding FSTD and improving extraction precision of NkiExtractor.