Entity profiling (EP) as an important task of Web mining and information extraction (IE) is the process of extracting entities in question and their related information from given text resources. From computational viewpoint, the Farsi language is one of the less-studie More
Entity profiling (EP) as an important task of Web mining and information extraction (IE) is the process of extracting entities in question and their related information from given text resources. From computational viewpoint, the Farsi language is one of the less-studied and less-resourced languages, and suffers from the lack of high quality language processing tools. This problem emphasizes the necessity of developing Farsi text processing systems. As an element of EP research, we present a semantic approach to extract profile of person entities from Farsi Web documents. Our approach includes three major components: (i) pre-processing, (ii) semantic analysis and (iii) attribute extraction. First, our system takes as input the raw text, and annotates the text using existing pre-processing tools. In semantic analysis stage, we analyze the pre-processed text syntactically and semantically and enrich the local processed information with semantic information obtained from a distant knowledge base. We then use a semantic rule-based approach to extract the related information of the persons in question. We show the effectiveness of our approach by testing it on a small Farsi corpus. The experimental results are encouraging and show that the proposed method outperforms baseline methods.
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The prior relation extraction approaches were relation-specific and supervised, yielding new instances of relations known a priori. While effective, this model is not applicable in case when the number of relations is high or where the relations are not known a priori. More
The prior relation extraction approaches were relation-specific and supervised, yielding new instances of relations known a priori. While effective, this model is not applicable in case when the number of relations is high or where the relations are not known a priori. Open Information Extraction (OIE) is a relation-independent extraction paradigm designed to extract relations directly from massive and heterogeneous corpora such as Web. One of the main challenges for an Open IE system is estimating the probability that its extracted relation is correct. A confidence measure shows that how an extracted relation is a correct instance of a relation among entities.
This paper proposes a new method of confidence estimation for OIE called Relation Confidence Estimator for Open Information Extraction (RCE-OIE). It investigates the incorporation of some proposed features in assigning confidence metric using logistic regression. These features consider diverse lexical, syntactic and semantic knowledge and also some extraction properties such as number of distinct documents from which extractions are drawn, number of relation arguments and their types. We implemented proposed confidence measure on the Open IE systems’ extractions and examined how it affects the performance of results. Evaluations show that incorporation of designed features is promising and the accuracy of our method is higher than the base methods while keeping almost the same performance as them. We also demonstrate how semantic information such as coherence measures can be used in feature-based confidence estimation of Open Relation Extraction (ORE) to further improve the performance.
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