Artificial intelligence (AI) is a new technological science for studying and developing theories, methods, techniques, and application systems that simulate, extend, and expand human intelligence. As a branch of computer science, the AI seeks to understand the essence of intelligence and produces a new type of intelligent machine that responds in a similar manner to human intelligence. Study in this field comprises robotics, speech recognition, image recognition, natural language processing, and expert systems, etc. The natural language processing in the field of AI is an important direction in the field of computer science and AI. It researches various theories and methods which can realize an effective communication between a person and a computer in a natural language. Generating a parallel text in same language and having similar semantics is an important constituent of the natural language processing. There are many application occasions for the parallel text in the same language. As an example, at present, when a search engine is searching a query sentence inputted by a user, since the user is unconstraint when inputting the query sentence, the effect tends to be inferior if the search is performed by using the query sentence inputted by the user. To be able to achieve a better search result, a parallel text in the same language is usually generated for the query sentence, and then the search is performed by using the generated parallel text in the same language.
However, at present, an approach to generate a parallel text in the same language of a text is generally to generate a substitution dictionary based on a parallel corpus, by using a statistical alignment algorithm or a rule alignment algorithm. Then, a parallel text in the same language after being replaced is generated according to an a priori knowledge and a substitution dictionary. In the existing approach of generating the parallel text in the same language, the alignment algorithm is complicated and requires much manual intervention, resulting in the generated substitution dictionary having a low accuracy rate. The substitution dictionary also needs to be stored. Generally, the size of the storage space required by the substitution dictionary is thousands of megabytes. Therefore, there exists a problem that the required storage space is excessive.
USPTO Backgrounds
USPTO Backgrounds