Singer Recognition and Semantic Role Labeling for Opinion Target Extraction from Social Network

摘要

Social network is a good resource to collect public opinions considering the diversity and variety in fashion, especially user generated content (UGC). UGC is defined as any type of content that created by users which could be pictures, videos, texts, comment, etc. Extracting the opinions from UGC can be the base of commercial policy, so how to extract the opinions correctly is an important problem. A common method is to regard mention times of entities as important indicator of network volume. There are two problems about the network volume: Are the opinions really talking about the target entities? Or the amount of opinions is enough for network volume analysis? There are several features about UGC, the various written format of entities and the fragmentary structure of sentences. The former means there may have nickname or punctuations in the entities and may drop the performance of NER. The latter means users write the sentences but omit part of words which may drop the performance of SRL. These problems of NER and SRL will also drop the performance of opinion target detection. Therefore, a great challenge is how to recognize entities and semantic role in large UGC corpora. In this study, we combine Named Entity Recognition (NER) and Semantic Role Labeling (SRL) to detect the opinion target (OTD) from UGC. In NER task, we compare the performance between CRF++[14] and neuron network models. In SRL task, we use highway connection and additional features to improve the performance. Finally, we design the rule to combined the result of NER and SRL for OTD task. The result show that our NER model gets 44% F1 on out-of-vocabulary entities extraction. On SRL task and OTD task, we get 71% F1 and 73% precision respectively.