Ryo Nagata

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.

Phrase Structure Annotation and Parsing for Learner English.

There has been almost no work on phrase structure annotation and parsing specially designed for learner English despite the fact that they are useful for representing the structural characteristics of learner English. To address this problem, in this paper, we first propose a phrase structure annotation scheme for learner English and annotate two different learner corpora using it. Second, we show their usefulness, reporting on (a) inter-annotator agreement rate, (b) characteristic CFG rules in the corpora, and (c) parsing performance on them. In addition, we explore methods to improve phrase structure parsing for learner English (achieving an F -measure of 0.878). Finally, we release the full annotation guidelines, the annotated data, and the improved parser model for learner English to the public.

Correcting Preposition Errors in Learner English Using Error Case Frames and Feedback Messages

This paper presents a novel framework called error case frames for correcting preposition errors. They are case frames specially designed for describing and correcting preposition errors. Their most distinct advantage is that they can correct errors with feedback messages explaining why the preposition is erroneous. This paper proposes a method for automatically generating them by comparing learner and native corpora. Experiments show (i) automatically generated error case frames achieve a performance comparable to conventional methods; (ii) error case frames are intuitively interpretable and manually modifiable to improve them; (iii) feedback messages provided by error case frames are effective in language learning assistance. Considering these advantages and the fact that it has been difficult to provide feedback messages by automatically generated rules, error case frames will likely be one of the major approaches for preposition error correction.

Exploiting learners' tendencies for detecting english determiner errors

This paper proposes a method for detecting determiner errors, which are highly frequent in learner English. To augment conventional methods, the proposed method exploits a strong tendency displayed by learners in determiner usage, i.e., mistakenly omitting determiners most of the time. Its basic idea is simple and applicable to almost any conventional method. This paper combines this idea with countability prediction, which outperforms the conventional methods, achieving an F-measure of 0.613.

Adaptive Spelling Error Correction Models for Learner English

Abstract Spelling errors are a characteristic of learner English and degrade the performances of natural language processing systems targeting English learners. This paper describes a method specially designed for automatically correcting spelling errors in learner English that reduces the effects from noise (e.g., grammatical and spelling errors) by adaptively creating spelling error correction models from raw learner corpora. An evaluation shows that the proposed method outperforms previous edit-distance-based and language-model-based methods. We also report results of an investigation into what types of spelling errors English learners tend to make, using the spelling error models created by the proposed method as a tool for our analysis.

The chanty bear: a new application for hri research

This paper presents yet another English-teaching robot, while putting emphasis on the merits which are offered by second language education to human robot interaction (HRI) research. The chanty bear, our prototype robot based on a rhythmic teaching method of English called Jazz Chants is introduced.

A Topic-Independent Method for Automatically Scoring Essay Content Rivaling Topic-Dependent Methods

This paper proposes a topic-independent method for automatically scoring essay content. Unlike conventional topic-dependent methods, it predicts the human score of a given essay without training essays written to the same topic as the target essay. To achieve this, this paper introduces a new measure called MIDF that measures how important and relevant a word is in a given essay. The proposed method predicts the score relying on the distribution of MIDF. Surprisingly, experiments show that the proposed method achieves an accuracy of 0.848 and performs as well as or even better than conventional topic-dependent methods.