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

Based on the breakthroughs in natural language interface agent “CAIWA,” web content distribution platform “CAIWA Channel” has been built for both PC and smatphone clients. The platform is regarded as wisdom media, since it seeks for providing information precisely what users demand, in addition to removing barriers of user interface bottlenecks. Not only recommendations of information to the user based on the past behavior, interest and profile, but also the CAIWA Channel has built-in evolution mechanism by proactively collecting information about the user in a natural manner via conversation with the user. It can accumulate knowledge for better meeting the user's personal requirements, while web contents are organized according to the user's interest. CAIWA Channel is not a mere information search system but a knowledge query system and a human-touch system incorporating emotional reactions.

Wisdom media “CAIWA Channel” based on natural language interface agent

Nonparametric Speaker Recognition Method Using Earth Mover's Distance(Speaker Recognition, Statistical Modeling for Speech Processing)

A practical sightseeing question answering system based on integrated knowledge-base (特集 知識情報化社会を支えるシステム技術)

Recently, there has been a growth in the amount of machine readable information pertaining to the biomedical field. With this growth comes a desire to be able to extract information, answer questions, etc. based on the information in the documents. Many of these desired tasks require sophisticated language processing algorithms, such as part-of-speech tagging, parsing, and semantic interpretation. In order to use these algorithms the text must first be cleansed of acronyms, abbreviations, and misspellings. In this paper we look at identifying, expanding, and disambiguating acronyms in biomedical texts. We present an integrated system that combines previously used methods for dealing with acronyms and Natural Language Processing techniques in new way for a new domain. The result is an integrated system that achieves a high precision and recall. We break the task up into three modular steps: Identification, Expansion, and Disambiguation. During identification, each word is examined to determine if it is an acronym or not. For this, a hybrid approach that is composed of a Naive Bayesian classifier and a set of handcrafted rules is used. We are able to achieve results of 99.96% accuracy with a small training set. During the expansion step, a list of possible meanings for the words determined to be acronyms is created. We break the expansion up into two categories, local and global expansion. For local expansion we use windowing and longest common subsequence to generate the possible expansions. Global expansion requires an acronym database to retrieve the possible expansions. The disambiguation step takes the list of possible meanings and determines which meaning is the correct one. To disambiguate the different candidate expansions we use WordNet and semantic similarity. Overall we obtain a recall and precision of over 91%. Keywords: Acronyms, Text Cleansing, Bioinformatics

/pdf/dealing-with-acronyms-in-biomedical-texts-1embpkxvbg.pdf

Dealing with Acronyms in Biomedical Texts

Data-driven function tag assignment has been studied for English using Penn Tree-bank data. In this paper, we address the question of whether such method can be applied to other languages and Tree-bank resources. In addition to simply extend previous method from English to Chinese, we also proposed an effective way to recognize function tags directly from lexical information, which is easily scalable for languages that lack sufficient parsing resources or have inherent linguistic challenges for parsing. We investigated a supervised sequence learning method to automatically recognize function tags, which achieves an F-score of 0.938 on gold-standard POS (Part-of-Speech) tagged Chinese text -- a statistically significant improvement over existing Chinese function label assignment systems. Results show that a small number of linguistically motivated lexical features are sufficient to achieve comparable performance to systems using sophisticated parse trees.

/pdf/accurate-learning-for-chinese-function-tags-from-minimal-1tc3apzigv.pdf

Fuji Ren

Papers

Wisdom media “CAIWA Channel” based on natural language interface agent

Nonparametric Speaker Recognition Method Using Earth Mover's Distance(Speaker Recognition, Statistical Modeling for Speech Processing)

A practical sightseeing question answering system based on integrated knowledge-base (特集知識情報化社会を支えるシステム技術)

Dealing with Acronyms in Biomedical Texts

Accurate Learning for Chinese Function Tags from Minimal Features

Fuji Ren

Papers

Wisdom media “CAIWA Channel” based on natural language interface agent

Nonparametric Speaker Recognition Method Using Earth Mover's Distance(Speaker Recognition, Statistical Modeling for Speech Processing)

A practical sightseeing question answering system based on integrated knowledge-base (特集 知識情報化社会を支えるシステム技術)

Dealing with Acronyms in Biomedical Texts

Accurate Learning for Chinese Function Tags from Minimal Features

A practical sightseeing question answering system based on integrated knowledge-base (特集知識情報化社会を支えるシステム技術)