Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

Emotion and Adaptation

Current question answering (QA) systems usually contain named entity recognizer (NER) as a core component. NER is an important and difficult task in computational linguistics. It plays an important role in natural language processing application such as Question Answering, Machine Translation, and Information Retrieval etc. NER includes the identification and classification of certain proper nouns (like location, organization, person, data, money and others) in a text. The purpose of our study is to recognize and extract the exact Japanese sightseeing domain named entities. It is a basic step for the following processing: question analysis and keyword extraction information retrieval. As well as, through doing the named entity recognition, we consider that it can mine exact information from text document to respond to user. This paper describes how to do the Japanese sightseeing named entity recognition due to we are constructing a Japanese sightseeing question answering system. We adopt the hybrid method which combined with machine learning and rule-base method. In the experiment of Japanese sightseeing domain named entity recognition we have got excellent precision and recalling rates. It shows that our method is effective and can be used in a practical question answering system.

Japanese named entity recognition for question answering system

This work proposes an approach to address the problem of improving content selection in automatic text summarization by using probabilistic neural network (PNN). This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features in combination to train the probabilistic neural network (PNN) in order to construct a text summarizer model.

Probabilistic neural network based text summarization

Being increasingly popular, the Internet greatly changes our live. We can conveniently receive and send information via the Internet. With the information explosion in Web, it is becoming crucial to develop means to automatically extract important sentences from the Web articles. In this paper, we propose a method which uses both statistical and structural information in sentence extraction. In addition, following the analysis of human's extractions, several heuristic rules are added to filter out non-important sentences and to prevent similar sentences from being extracted. Our experimental results proved the effectiveness of these means. In particular, once the heuristic rules being added, a significant improvement has been observed.

Automatic abstracting important sentences of web articles

Textual information is an important communication medium contained rich expression of emotion, and emotion recognition on text has wide applications. Word emotion analysis is fundamental in the problem of textual emotion recognition. Through an analysis of the characteristics of word emotion expression, we use word emotion vector to describe the combined basic emotions in a word, which can be used to distinguish direct and indirect emotion words, express emotion ambiguity in words, and express multiple emotions in words. Based on Ren-CECps (a Chinese emotion corpus), we do an experiment to explore the role of emotion word for sentence emotion recognition and we find that the emotions of a simple sentence (sentence without negative words, conjunctions, or question mark) can be approximated by an addition of the word emotions. Then MaxEnt modeling is used to find which context features are effective for recognizing word emotion in sentences. The features of word, N-words, POS, Pre-N-words emotion, Pre-is-degree-word, Pre-is-negativeword, Pre-is-conjunction and their combination have been experimented. After that, we use the two metrics: Kappa coefficient of agreement and Voting agreement to measure the word annotation agreement of Ren-CECps. The experiments on above context features showed promising results compared with word emotion agreement on people's judgments.

/pdf/automatic-annotation-of-word-emotion-in-sentences-based-on-59qaqiepwp.pdf

Automatic Annotation of Word Emotion in Sentences Based on Ren-CECps.

Recently, human-computer interaction is become increasingly common in daily life, in which facial expression recognition has a wide application potential. At present, the state-of-the-art accuracy of facial expression classification based on the Kaggle database is only 71.20%, which still indicates some room for improvement. Based on the resnet-18 model, we find that a large number of parameters in the model are prone to over-fitting, which hinders the face recognition accuracy. In this paper, we propose a modified network model based on the resnet18-network, in which we change the original average pooling layer to a global average pooling layer with a double convolution layer. Our experiment result suggests that our model outperformed the state-of-the-art results showing a 1.49% increase in accuracy on the Kaggle database.

Fuji Ren

Papers

Japanese named entity recognition for question answering system

Probabilistic neural network based text summarization

Automatic abstracting important sentences of web articles

Automatic Annotation of Word Emotion in Sentences Based on Ren-CECps.

Facial Sentiment Classification Based on Resnet-18 Model