In this paper, we propose a joint algorithm for the word segmentation on Chinese social media. Previous work mainly focus on word segmentation for plain Chinese text, in order to develop a Chinese social media processing tool, we need to take the main features of social media into account, whose grammatical structure is not rigorous, and the tendency of using colloquial and Internet terms makes the existing Chinese-processing tools inefficient to obtain good performance on social media. In our approach, we combine CRF and MMSEG algorithm and extend features of traditional CRF algorithm to train the model for word segmentation, We use Internet lexicon in order to improve the performance of our model on Chinese social media. Our experimental result on Sina Weibo shows that our approach outperforms the state-of-the-art model.
Currently research on natural language process for social media is still at the starting stage, in the 2006 SIGHAN Chinese word segmentation competition, approaches based on sequence annotation has been widely used. Microsoft used conditional random field and the size of feature window equals one. @cite_0 It turns out that features have been simplified, but the performance was still very good. DaLian Science and Technology University built two models, one is based on the use of the word CRF model, the other is based on MMSM model. During our experiments, we used CRF algorithm as well, and it plays the key role when we build the model. Our work mainly depends on the improvement of conditional random field.
@cite_0
2358307482
In this paper, we describe a Chinese word segmentation system that we developed for the Third SIGHAN Chinese Language Processing Bakeoff (Bakeoff2006). We took part in six tracks, namely the closed and open track on three corpora, Academia Sinica (CKIP), City University of Hong Kong (CityU), and University of Pennsylvania University of Colorado (UPUC). Based on a conditional random field based approach, our word segmenter achieved the highest F measures in four tracks, and the third highest in the other two tracks. We found that the use of a 6-tag set, tone feature of Chinese character and assistant segmenters trained on other corpora further improve Chinese word segmentation performance. Chinese word segmentation is a difficult, important and widely-studied sequence modeling problem. This paper demonstrates the ability of linear-chain conditional random fields (CRFs) to perform robust and accurate Chinese word segmentation by providing a principled framework that easily supports the integration of domain knowledge in the form of multiple lexicons of characters and words. We also present a probabilistic new word detection method, which further improves performance. Our system is evaluated on four datasets used in a recent comprehensive Chinese word segmentation competition. State-of-the-art performance is obtained. We present a Chinese word segmentation system submitted to the closed track of Sighan bakeoff 2005. Our segmenter was built using a conditional random field sequence model that provides a framework to use a large number of linguistic features such as character identity, morphological and character reduplication features. Because our morphological features were extracted from the training corpora automatically, our system was not biased toward any particular variety of Mandarin. Thus, our system does not overfit the variety of Mandarin most familiar to the system's designers. Our final system achieved a F-score of 0.947 (AS), 0.943 (HK), 0.950 (PK) and 0.964 (MSR). We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, that is the surface forms of morphemes. We extend a recent segmentation approach based on conditional random fields from purely supervised to semi-supervised learning by exploiting available unsupervised segmentation techniques. We integrate the unsupervised techniques into the conditional random field model via feature set augmentation. Experiments on three diverse languages show that this straightforward semi-supervised extension greatly improves the segmentation accuracy of the purely supervised CRFs in a computationally efficient manner.