Personal Attributes Extraction in Unstructured ChineseText Task is a subtask of The 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP-2014). In this report, we propose a method based on the combination of trigger words, dictionary and rules to realize the personal attributes extraction. We introduce the extraction process and show the result of this bakeoff, which can show that our method is feasible and has achieved good effect.

Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary sentences. A lot of work have been done for English Open IE, and now the Chinese Open IE field is attracting more and more researchers and scholars. In this paper we present a novel SCOERE (Semi-supervised Chinese Open Entity Relation Extraction) method. This approach combines the advantages of both unsupervised and supervised methods, which needs very little human work to annotate the corpus and would iteratively extract tuples until there is no new relation keywords generated. The experiments show that our method could get a good recall rate and a reasonable accuracy rate.