MinorThird 서울시립대학교 인공지능연구실 곽별샘

MinorThird A collection of Java classes for  Storing text  Annotating text  Learning to extract entities  Categorize text

What's Different About MinorThird Differs from existing NLP and learning toolkits  Combines tools for annotating and visualizing text with state-of-the art learning methods  Contains methods to visualize Both training data and the performance of classifiers Facilitates debugging  Integrated with text manipulation tools Possible to track and visualize the transformation of text data into machine learning data  Architected to support active learning and on-line learning Should facilitate integration of learning methods into agents

Components TextBase  A collection of documents TextLabels  Logical assertions about documents in a TextBase  A type of stand off annotation The annotation are completely independent of the text  Assert a category or property for a word, a document, or a subsequence of words(span) by human labelers or by a learned program encode  syntactic properties  like shallow parser or POS tags  semantic properties  like the functional role that entities play in a sentence

Components Repository  Annotated TextBases are accessed in a single uniform way. However, they are stored in one of several schemes.  Repository can be configured to hold a bunch of TextLabels and their associated TextBases. Mixup (Minorthird Information eXtraction and Understanding Program)  A special-purpose annotation language Moderately complex hand-coded annotation programs can be implemented with Mixup Based on the widely used notion of cascaded finite state transducers Includes some powerful features  A GUI debugging environment  Escape to Java  A kind of subroutine call mechanism