Tsutomu Hirao

This paper presents a new approach to exploit coreference information for extracting event-argument (E-A) relations from biomedical documents. This approach has two advantages: (1) it can extract a large number of valuable E-A relations based on the concept of salience in discourse; (2) it enables us to identify E-A relations over sentence boundaries (cross-links) using transitivity of coreference relations. We propose two coreference-based models: a pipeline based on Support Vector Machine (SVM) classifiers, and a joint Markov Logic Network (MLN). We show the effectiveness of these models on a biomedical event corpus. Both models outperform the systems that do not use coreference information. When the two proposed models are compared to each other, joint MLN outperforms pipeline SVM with gold coreference information.

In this paper, we describe a method of automatic sentence alignment for building extracts from abstracts in automatic summarization research. Our method is based on two steps. First, we introduce the "dependency tree path" (DTP). Next, we calculate the similarity between DTPs based on the ESK (Extended String Subsequence Kernel), which considers sequential patterns. By using these procedures, we can derive one-to-many or many-to-one correspondences among sentences. Experiments using different similarity measures show that DTP consistently improves the alignment accuracy and that ESK gives the best performance.

近年, インターネットや大容量の磁気デバイスの普及によって, 大量の電子化文書が氾濫している. こうした状況を背景として, 文書要約技術に対する期待が高まってきている. 特に, ある話題に関連する一連の文書集合をまとめて要約することが可能となれば, 人間の負担を大きく軽減することができる. そこで本稿では, 特定の話題に直接関連する文書集合を対象とし, 機械学習手法を用いることによって重要文を抽出する手法を提案する. 重要文抽出の手法としては近年, 自然言語処理研究の分野でも注目されている機械学習手法の1種であるSupport Vector Machineを用いた手法を提案する. 毎日新聞99年1年分より選んだ12話題の文書集合を用意し, それぞれの話題から総文数の10%, 30%, 50%の要約率に応じて人手により重要文を抽出した正解データセットを異なる被験者により3種作成した. このデータセットを用いて評価実験を行った結果, 提案手法の重要文抽出精度は, Lead手法, TF・IDF手法よりも高いことがわかった. また, 従来より複数文書要約に有効とされる冗長性の削減が, 文を単位とした場合には, 必ずしも有効でないこともわかった.

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.