Yuji Matsumoto

We explore the use of morphological analysis as preprocessing for protein name tagging. Our method finds protein names by chunking based on a morpheme, the smallest unit determined by the morphological analysis. This helps to recognize the exact boundaries of protein names. Moreover, our morphological analyzer can deal with compounds. This offers a simple way to adapt name descriptions from biomedical resources for language processing. Using GENIA corpus 3.01, our method attains f-score of 70 points for protein molecule names, and 75 points for protein names including molecules, families and domains.

Extracting sentences that contain important information from a document is a form of text summarization. The technique is the key to the automatic generation of summaries similar to those written by humans. To achieve such extraction, it is important to be able to integrate heterogeneous pieces of information. One approach, parameter tuning by machine learning, has been attracting a lot of attention. This paper proposes a method of sentence extraction based on Support Vector Machines (SVMs). To confirm the method's performance, we conduct experiments that compare our method to three existing methods. Results on the Text Summarization Challenge (TSC) corpus show that our method offers the highest accuracy. Moreover, we clarify the different features effective for extracting different document genres.

The paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In particular, we explore the use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries. The corpus of human created extracts is created from a newspaper corpus and used as a test set. We build probabilistic decision trees of different flavors and integrate each of them with the clustering framework. Experiments with the corpus demonstrate that the mixture of the two paradigms generally gives a significant boost in performance compared to cases where either of the two is considered alone.