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.

To acquire a second language, one must develop an ear and tongue for the correct stress and intonation patterns of that language. In English language teaching, there is an effective method called Jazz Chants for working on the sound system. In this paper, we propose a method for predicting stressed words, which play a crucial role in Jazz Chants. The proposed method is specially designed for stress prediction in Jazz chants. It exploits several sources of information including words, POSs, sentence types, and the constraint on the number of stressed words in a chant text. Experiments show that the proposed method achieves an F-measure of 0.939 and outperforms the other methods implemented for comparison. The proposed method is expected to be useful in supporting non-native teachers of English when they teach chants to students and create chant texts with stress marks from arbitrary texts.

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.

This paper describes a method for recognizing romanized Japanese words in learner English. They become noise and problematic in a variety of systems and tools for language learning and teaching including text analysis, spell checking, and grammatical error detection because they are Japanese words and thus mostly unknown to such systems and tools. A problem one encounters when recognizing romanized Japanese words in learner English is that the spelling rules of romanized Japanese words are often violated. To address this problem, the described method uses a clustering algorithm reinforced by a small set of rules. Experiments show that it achieves an F-measure of 0.879 and outperforms other methods. They also show that it only requires the target text and an English word list of reasonable size.

This paper proposes a topic-independent method for automatically scoring essay content. Unlike conventional topic-dependent methods, it predicts the human-assigned 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.

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 also proposes combining the method with countability prediction, which results in further improvement. Experiments show that the proposed method achieves an F-measure of 0.684 and significantly outperforms conventional methods.

Highly conductive poly(3,4-ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT,:,PSS) attracts a strong attention as a transparent electrode material since it may replace indium tin oxide (ITO) electrodes used in many organic semiconductor devices. However, PEDOT,:,PSS films have been usually deposited using acidic precursors, which caused long term device degradation as well as safety issues during device fabrication processes. This paper firstly reports application of highly conductive PEDOT,:,PSS films deposited on polyethylene terephthalate (PET) substrates using a neutralized precursor to organic bulkhetrojunction solar cells. The sheet resistance ($R_{s}$) of PEDOT,:,PSS was reduced by more than two orders of magnitudes by spin coating the neutralized solution containing 5% of dimethyl sulfoxide (DMSO) and dipping the films in DMSO for 30,min. Subsequently, an approximately 55 nm-thick PEDOT,:,PSS layer was obtained with $R_{s}$ =159 $Omega$/$square$, a conductivity of 1143 S/m, and an optical transmittance of 84%. A solar cell based on poly[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b$'$]dithiophene-2,~6-diyl][3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,~4-b]thiophenediyl]: [6,6]-phenyl-C$_{71}$-butyric acid methyl ester fabricated on the PEDOT: PSS/PET substrate exhibited a higher open circuit voltage and power conversion efficiency than did a control solar cell fabricated on an ITO-coated PET substrate. These results suggest that the highly conductive PEDOT,:,PSS films may contribute to realize ITO-free flexible organic solar cells.

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 previous 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 using automatically generated rules, error case frames will likely be one of the major approaches for preposition error correction.