Semantic Knowledge Acquisition from Blogs with Tag-Topic Model

This paper focuses on semantic knowl- edge acquisition from blogs with the proposed tag- topic model. The model extends the Latent Dirichlet Allocation （LDA） model by adding a tag layer be- tween the document and the topic. Each document is represented by a mixture of tags; each tag is as- sociated with a multinomial distribution over topics and each topic is associated with a multinomial dis- trNution over words. After parameter estimation, the tags are used to descrNe the underlying topics. Thus the latent semantic knowledge within the top- ics could be represented explicitly. The tags are treated as concepts, and the top-N words from the top topics are selected as related words of the con- cepts. Then PMI-IR is employed to compute the re- latedness between each tag-word pair and noisy words with low correlation removed to improve the quality of the semantic knowledge. Experiment re- sults show that the proposed method can effectively capture semantic knowledge, especially the polyse- me and synonym.

Origin of summer monsoon rainfall identified by δ^(18)O inprecipitation

A negative correlation between δ 18O in mon-soon precipitation and f, the ratio of precipitable water in monsoon region to that in water source area, is hypothesized. Using the Rayleigh model, a new method for identifying ori-gin of summer monsoon rainfall is developed based on the hypothesis. In order to validate the method, the isotopic data at New Delhi, a typical station in the southwest monsoon region, and Hong Kong, a typical station in the southeast monsoon region, were collected and analyzed for case studies. The case studies indicate that the water source areas of the monsoon rainfall at the two stations identified by the method are accordant with the general atmosphere circulation pat-terns. The method developed in this paper is significantly important for tracing the origin of summer monsoon pre-cipitation.

Topic modeling for large-scale text data

This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named ＇stochastic variational inference＇ and ＇SGRLD＇, our algorithm achieves a faster convergence rate and better performance.