Abstract
Currently, large amounts of information exist in Web sites and various digital media. Most of them are in natural lan-guage. They are easy to be browsed, but difficult to be understood by computer. Chunk parsing and entity relation extracting is important work to understanding information semantic in natural language processing. Chunk analysis is a shallow parsing method, and entity relation extraction is used in establishing relationship between entities. Because full syntax parsing is complexity in Chinese text understanding, many researchers is more interesting in chunk analysis and relation extraction. Conditional random fields (CRFs) model is the valid probabilistic model to segment and label sequence data. This paper models chunk and entity relation problems in Chinese text. By transforming them into label solution we can use CRFs to realize the chunk analysis and entities relation extraction.
Highlights
At present, information is presented in various digital media
Chunk analysis is a shallow parsing method, and entity relation extraction is used in establishing relationship between entities
Because full syntax parsing is complexity in Chinese text understanding, many researchers is more interesting in chunk analysis and relation extraction
Summary
Information is presented in various digital media. Many of them are organized in natural language, such as information in Web pages, text document in digital library etc. Information extraction is a process to retrieve information from large text set It may be concerned with identifying named entity, extracting relationship and label properties of sentence etc. It is a subfield of natural language understanding. Considered the problem of relation extraction in the context of natural language parsing and augmented syntactic parses with semantic relation-specific attributes [8] It will be critical in events detecting and describing for research on information extraction. In Chinese understanding, some research use CRFs in Chinese part-of-speech and word segmentation [10,11], but seldom in chunk parsing and entity relation extraction.
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