About

In today's world, there is a large amount of unstructured written sources that contain structured information. An example is police reports. They contain information about entities (names, addresses, phone numbers, weapons, drugs, etc..) and the relationships between them. For almost any automatic processing, this information must be transformed into some representation which can be easily read by computers. For example, it can be written into the database and assigned to some already existing records stored in the system. This approach is indeed used in practice but the whole process is performed manually, which is very time consuming. Similarly, the same time-consuming processing of documents can be found in other institutions or companies. Such problem of extracting structure information out of general documents has recently gained attention from both research and industry.

The topic of our software project called Text Analyser (TextAn) is to deliver a pleasant and effective tool for extracting structured data from texts. In many areas including our police example, the accuracy of the extracted information is critical and thus it does not permit fully automatic extraction. Therefore, we keep the human element involved in the process to correct and adjust automatic results. In turn, we exploit these interventions with machine learning techniques, so the results improve in time.

Although the first impulse indeed came from the needs of a department of the Czech Police, TextAn is made as general as possible to allow easy configuration for any other domain as needed. It also is not language dependent, so it can process texts in completely different languages if corresponding linguistic components are provided.

TextAn is a client-server application for text analysis. It provides webservice interfaces, so it is easy to implement a custom client to meet specific user needs or to integrate it in a legacy system. The project consists of two main contributions. Firstly, the server integrating named entity recognition and object assigning provides interface to access the database and these components. Secondly, the client offers a friendly method for users to confirm or adjust the system's suggestions and explore the data stored in the database.

TextAn is implemented in Java 8 and it is platform independent except for some third party libraries that are attached only for some platforms. However they are open source and can be compiled for any operating system desired. The data can be stored within any SQL-like relation database, e.g. MySQL which is supported out-of-the-box.