Atsuo Kawai

A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English

This paper proposes a method for detecting errors in article usage and singular plural usage based on the mass count distinction. First, it learns decision lists from training data generated automatically to distinguish mass and count nouns. Then, in order to improve its performance, it is augmented by feedback that is obtained from the writing of learners. Finally, it detects errors by applying rules to the mass count distinction. Experiments show that it achieves a recall of 0.71 and a precision of 0.72 and outperforms other methods used for comparison when augmented by feedback.

Detecting article errors based on the mass count distinction

This paper proposes a method for detecting errors concerning article usage and singular/plural usage based on the mass count distinction. Although the mass count distinction is particularly important in detecting these errors, it has been pointed out that it is hard to make heuristic rules for distinguishing mass and count nouns. To solve the problem, first, instances of mass and count nouns are automatically collected from a corpus exploiting surface information in the proposed method. Then, words surrounding the mass (count) instances are weighted based on their frequencies. Finally, the weighted words are used for distinguishing mass and count nouns. After distinguishing mass and count nouns, the above errors can be detected by some heuristic rules. Experiments show that the proposed method distinguishes mass and count nouns in the writing of Japanese learners of English with an accuracy of 93% and that 65% of article errors are detected with a precision of 70%.

A Statistical Model Based on the Three Head Words for Detecting Article Errors

In this paper, we propose a statistical model for detecting article errors, which Japanese learners of English often make in English writing. It is based on the three head words --- the verb head, the preposition, and the noun head. To overcome the data sparseness problem, we apply the backed-off estimate to it. Experiments show that its performance (F-measure=0.70) is better than that of other methods. Apart from the performance, it has two advantages: (i) Rules for detecting article errors are automatically generated as conditional probabilities once a corpus is given; (ii) Its recall and precision rates are adjustable.

Reinforcing English Countability Prediction with One Countability per Discourse Property

Countability of English nouns is important in various natural language processing tasks. It especially plays an important role in machine translation since it determines the range of possible determiners. This paper proposes a method for reinforcing countability prediction by introducing a novel concept called one countability per discourse. It claims that when a noun appears more than once in a discourse, they will all share the same countability in the discourse. The basic idea of the proposed method is that mispredictions can be correctly overridden using efficiently the one countability per discourse property. Experiments show that the proposed method successfully reinforces countability prediction and outperforms other methods used for comparison.

A unified image segmentation approach with application to bread recognition

We describe a unique segmentation approach that can be used in a machine vision based cash register system for commodity pricing of hand made bread. Systematic sorting and classification of bread samples according to their shapes, sizes, textures and surface color distribution is explored. We compare and contrast our approach with character recognition and face detection methods.

A Method for Reinforcing Noun Countability Prediction

This paper proposes a method for reinforcing noun countability prediction, which plays a crucial role in demarcating correct determiners in machine translation and error detection. The proposed method reinforces countability prediction by introducing a novel heuristics called one countability per discourse. It claims that when a noun appears more than once in a discourse, all instances will share identical countability. The basic idea of the proposed method is that mispredictions can be corrected by efficiently using one countability per discourse heuristics. Experiments show that the proposed method successfully reinforces countability prediction and outperforms other methods used for comparison. In addition to its performance, it has two advantages over earlier methods: (i) it is applicable to any countability prediction method, and (ii) it requires no human intervention to reinforce countability prediction.

Effectiveness of Relative Expressions for Trend Information Extraction

本論文では, 新聞記事における相対表現の調査および動向情報抽出のための相対表現の利用効果について議論する. 相対表現とは, 『12%増』『昨年』『第一位』のように, 動向に関連した数量表現や時間表現において数値の相対的差異や数値変動を示す表現である. 相対表現の処理が可能となれば, テキストからの動向情報抽出はより効率的に行える. 我々は, 新聞記事中に出現する相対表現を調査し, 相対表現の役割と出現傾向について分析した. そして, 動向情報把握のために必要な基本情報の抽出規則を構築した. 抽出規則がどの程度動向情報抽出に有効であるかを検証するために実験を行った. その結果, F値で0.8以上の性能が得られ, 相対表現を利用した動向情報抽出の可能性を確認することができた.

A method for distinguishing English mass and count nouns

日本人英語学習者が書いた英文に多く見られる冠詞の誤りや単数/複数の使い分けに関する誤りを検出するためには, 名詞の可算/不可算の判定が重要である.そこで, 本論文では, 文脈情報に基づいた英語名詞の可算/不可算判定手法を提案する.提案手法では, 決定リストを用いて可算/不可算の判定を行う.決定リストは, 判定対象となっている名詞の可算/不可算の例からなる学習データから学習される.一般に, 学習データの作成は人手で行われるため, 費用と時間を要するという問題がある.この問題を解決するため, 本論文では学習データをコーパスから自動生成する手法も提案する.従って, 提案手法では, コーパスが与えられると決定リストの学習が行われる.学習された決定リストは, 文脈情報のみに基づいて可算/不可算の判定を行うため, 上記誤りの検出に応用可能である.実験の結果, 提案手法の可算/不可算の判定精度は83.9%であることが確認された.