Preparation of Ce-MOF@TEOS to enhance the anti-corrosion properties of epoxy coatings

As a type of crowdsensing media, micro-blog has become an important crowdsensing place for a lot of real-time information dissemination and discussion. With the increasing of micro-blog users, there are more and more new topics emerging on this kind of platform, which has made the users difficult in finding out their own interesting topics. To solve this problem, this paper proposes a micro-blog topic recommendation system which can give corresponding suggestions/strategies for users. Firstly, the user relationship (i.e., a user adds a follow hyperlink to another user) in micro-blog can be effectively analyzed and saved to the user graph. In addition, an algorithm of computing user authority (which is similar to the idea of PageRank) is proposed to catch influential users based on the built user graph. Secondly, Topic Feature Graph (TFG) and User Micro-blog Feature Graph (UMFG) are respectively constructed based on the micro-blog text corpus of a topic and the micro-blog texts followed by a given user. Based on TFG and UMFG, User Topic Feature Vector (UTFV) and User Topic Feature Matrix (UTFM) can be achieved. After that, users' similarity is calculated based on the User Topic Feature Vector and User Topic Feature Matrix to realize the users clustering by the help of the hierarchical clustering algorithm. Incorporating topic heat degree and user authority, the recommendation algorithm is presented to realize Micro-blog topic personalized recommendation within user clustering set. Experiments show that our proposed recommendation system has a good accuracy which is up to 50.2%.

The Recommendation System of Micro-Blog Topic Based on User Clustering

Single document keyword extraction via quantifying higher-order structural features of word co-occurrence graph

This paper proposes a non-segmented document clustering method using self-organizing map (SOM) and frequent max substring technique to improve the efficiency of information retrieval. SOM has been widely used for document clustering and is successful in many applications. However, when applying to non-segmented document, the challenge is to identify any interesting pattern efficiently. There are two main phases in the propose method: preprocessing phase and clustering phase. In the preprocessing phase, the frequent max substring technique is first applied to discover the patterns of interest called Frequent Max substrings that are long and frequent substrings, rather than individual words from the non-segmented texts. These discovered patterns are then used as indexing terms. The indexing terms together with their number of occurrences form a document vector. In the clustering phase, SOM is used to generate the document cluster map by using the feature vector of Frequent Max substrings. To demonstrate the proposed technique, experimental studies and comparison results on clustering the Thai text documents, which consist of non-segmented texts, are presented in this paper. The results show that the proposed technique can be used for Thai texts. The document cluster map generated with the method can be used to find the relevant documents more efficiently.

https://researchrepository.murdoch.edu.au/22859/1/SOM%2DBased_Document_Clustering.pdf

A SOM-Based Document Clustering Using Frequent Max Substrings for Non-Segmented Texts

A document (or multiple documents) is analyzed to identify entities of interest within that document. This is accomplished by constructing n-gram or bi-gram models that correspond to different kinds of text entities, such as chemistry-related words and generic English words. The models can be constructed from training text selected to reflect a particular kind of text entity. The document is tokenized, and the tokens are run against the models to determine, for each token, which kind of text entity is most likely to be associated with that token. The entities of interest in the document can then be annotated accordingly.

System and method for extracting entities of interest from text using n-gram models

This paper presents a new kind of Chinese text encoding method based on Chinese word, and establishes a new Chinese document format which deals with the automatic segmentation issue. This method makes word the smallest information unit. Chinese text analysis does not rely on segmentation by the method. On this word platform, N-gram and word co-occurrence statistical analysis are combined to carry out Chinese keyword extraction experiment. Firstly, candidate keywords are extracted with bi-gram model. Then, a set of co-occurrences between every word in bi-grams and frequent words is generated. Co-occurrence distribution shows importance of every word. According to the analysis result, keywords are chosen from bi-grams. This algorithm applies to a single document without using a corpus, and experimental results are satisfying.

Chinese Keyword Extraction Based on N-Gram and Word Co-occurrence

The invention discloses a Chinese character information disposal method, which comprises the following steps: classifying the words in the > to form dictionary word; encoding the dictionary word into the dictionary code at four-byte pattern; corresponding the dictionary code to the set internal code of Chinese character to form dictionary code graph; adapting word as basic unit at phonetic pattern; inputting phonetic code by keyboard according to the corresponding relationship to form the dictionary code format file; The invention avoids the distribution word difficulty of Chinese information disposal without changing the computer inside structure and setting, which accomplishes the file display based on word.

Computer processing and keyboard inputting method for Chinese word

At present researches on Chinese keyword extraction mainly focus on automatic segmentation which is a pretreatment problem. This paper presents a kind of Chinese encoding method based on word platform, and establishes a new Chinese document format in computer. This method makes word the smallest information unit. Chinese keyword extraction does not rely on segmentation by this new method. Thereby the efficiency and quality could be improved. Statistical analysis is adopted to conduct the experiment of keyword extraction based on word platform, and experimental results are satisfying.

Chinese Keyword Extraction Based on Word Platform

This paper presents a word-based Chinese document experimental system which is aimed to make Chinese information processing technology to develop on a more reliable and more efficient basis. This system implements the document storage and processing format, both of which are based on the smallest information carrier: Chinese word. Further an IE algorithm with two steps strategy for the Chinese free text is introduced. And then taking this document system as experimental platform, choosing the abstract part of Chinese Sci_Tech journals as the free text, the IE experiment which is conducted and get good results: accuracy ratio P is 95.03%, recall ratio R is 91.40% and F-value is 93.18% From the experimental results, we can see that the Word-based Chinese Document System designed by us can promote the development of Chinese Information Processing technology to more advanced application stages.

Study of Word-Based Chinese Document Experimental System and Chinese Free-Text Information Extraction Experiment Based on It

This paper proposes the extraction task of the Chinese Sci-tech journal text and presents a WCA-Selection Chinese free-text HMM IE algorithm. The HMM IE algorithm takes the Chinese Sci-tech journal abstract text as the extraction text. According to the features of WCA, an idea of WCA selection model re-optimization is proposed. And a WCA selection optimization strategy is concreted. Then the experimental verification is conducted with a satisfied result. The experiment results show that the designed extraction algorithm and WCA selection optimization strategy have good performance in the the Chinese Sci-tech journal abstract text.

Hui Jiao

Papers

Chinese Keyword Extraction Based on N-Gram and Word Co-occurrence

Computer processing and keyboard inputting method for Chinese word

Chinese Keyword Extraction Based on Word Platform

Study of Word-Based Chinese Document Experimental System and Chinese Free-Text Information Extraction Experiment Based on It

Experimental study of Chinese free-text IE algorithm based on WCA-selection using Hidden Markov model