Research And Application Of Brand Recognition Based On HMM

Currently, the risk prevention policy of Baidu is not controlled fine, in which the scope of the limit rule is too wide. No matter what the industry is, as long as the advertisement involves vulgar and triggers the online risk, Baidu will don’t let it to be displayed on these search result pages. As a consequence, the effective keywords of company can’t be purchased and shown. So, Baidu lost a lot of inlet flow, got a high manslaughter rate, and also brought a large number of customer complaints. All of these lead that the risk of online advertisement cannot be controlled in time and the risk of online brand and competing products has been always high, resulting in the ratio of about 63% of the class of risky online brand risk reached.The thesis studied the status and algorithm of named entity recognition, analyzed the problem of Baidu risk prevention and control, and proposed a solution of brand recognition and application based on HMM (Hidden Markov Models). The solution is that after constructing a HMM model in the basis of mining the original corpus through industry distinguishing and role tagging, the Viterbi algorithm is used to predict brands, and the brand strategy vocabulary generated and the brand risk identification module are applied to the audit system through the service interfaces. As a result, the online risk of brand category is reduced, the experience of company and netizen is optimized, and the advertising revenue is improved also. The main work of this thesis is as follows:Firstly, this thesis introduces the research background and significance of the study, discusses the related named entity recognition and the problems of Baidu risk prevention and control, and gives a brief description for the structure of the thesis.Secondly, this thesis discusses an improved method of recognition algorithms, proposes an algorithm of brand recognition based on HMM. The work is divided as three parts:(1) using the way of distinguishing industry, segmenting the word and labeling the word by roles to process Baidu internal material audit log and material information to get pre-processing results; (2) while training the HMM, using the Viterbi algorithm to predict recognition of brand by automatically role tagging; (3) doing some experiments to analyze the results of the brand recognition. The experimental results show that the method has higher accuracy, recall and F value.Thirdly, this thesis discusses the application of the improved algorithm of brand recognition. The work is also separated as three parts:(1) using data mining and pretreatment by distinguishing the industry to obtain the original data; (2) applying the improved recognition algorithm to identify brand; (3) providing the service interfaces of the brand recognition for the applications in other systems, such as generating the brand strategy vocabulary to limit the industry by granularity of the rules and to reduce to manslaughter, and applying brand risk identification module in online advertising inspecting system to identify trademark risk and to reduce online risk.Fourthly, some systematic experiments and analysis were done, and the risk and the consumption influence of the brand application are described in the thesis. The analysis results show that the online risk is down to 57%, and the advertising revenue is improved up to billions RMB, which proves the effectiveness and practicability of the research work of this thesis.