Universal Dependencies for Learner English

IEICE Trans. Inf. & Syst.

Whittaker²

2017

SUMMARYThis 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.

Section: Discussionmentioning

confidence: 99%

A Method for Correcting Preposition Errors in Learner English with Feedback Messages

IEICE Trans. Inf. & Syst.

Whittaker²

2017

“…Note that most POS-tagging guidelines for learner English such as Ragheb and Dickinson (2012), , and Berzak et al (2016) stipulate that a token with an orthographic error should receive the POS label that is given to the corresponding correct spelling. Accordingly, it is preferable that POS taggers for learner English should do the same.…”

Section: Potential Causes Of Pos-tagging Errors In Learner Englishmentioning

confidence: 99%

“…5 POS labels based on distributional information can also be included by using the multiple layer scheme (Díaz-Negrillo et al, 2009;Dickinson and Ragheb, 2009;Berzak et al, 2016). It depends on the user which layer to use.…”

Section: Potential Causes Of Pos-tagging Errors In Learner Englishmentioning

confidence: 99%

A POS Tagging Model Adapted to Learner English

Proceedings of the 2018 EMNLP Workshop W-Nut: The 4th Workshop on Noisy User-Generated Text

Mizumoto

Kikuchi³

et al. 2018

There has been very limited work on the adaptation of Part-Of-Speech (POS) tagging to learner English despite the fact that POS tagging is widely used in related tasks. In this paper, we explore how we can adapt POS tagging to learner English efficiently and effectively. Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this. Considering the previous findings and the discussion, we introduce the design of our model based on bidirectional Long Short-Term Memory. In addition, we describe how to adapt it to a wide variety of native languages (potentially, hundreds of them). In the evaluation section, we empirically show that it is effective for POS tagging in learner English, achieving an accuracy of 0.964, which significantly outperforms the state-of-the-art POS-tagger. We further investigate the tagging results in detail, revealing which part of the model design does or does not improve the performance.

“…Table 2 shows some basic statistics on the two learner corpora. We note that the annotations are applied to original sentences as in Berzak et al (2016). Because the KJ corpus contains learner errors and the respective corrections, it allows us to use them for the phrase structure annotation.…”

Section: Datasetmentioning

confidence: 99%

“…We also show that a model trained on it can improve parsing performance (0.878 in Fmeasure) in Section 4. Note that there have been a very limited number of publicly available learner corpora that are syntactically annotated; to the best of our knowledge, Berzak et al (2016) recently released a publicly available learner corpus annotated with dependency (but not phrase structure). This inhibits effective investigations and applications using learner language corpora such as automated grammatical error correction, automated scoring and native language identification.…”

Section: Introductionmentioning

confidence: 99%

Phrase Structure Annotation and Parsing for Learner English

Sakaguchi

Journal of Natural Language Processing

2017

Learner English often contains grammatical errors with structural characteristics such as omissions, insertions, substitutions, and word order errors. These errors are not covered by the existing context-free grammar (CFG) rules. Therefore, it is not at all straightforward how to annotate learner English with phrase structures. Because of this limitation, there has been almost no work on phrase structure annotation for learner corpora despite its importance and usefulness. To address this issue, we propose a phrase structure annotation scheme for learner English, that consists of five principles. We apply the annotation scheme to two different learner corpora and show (i) its effectiveness at consistently annotating learner English with phrase structure (i.e., high inter-annotator agreement); (ii) the structural characteristics (CFG rules) of learner English obtained from the annotated corpora; and (iii) phrase structure parsing performance on learner English for the first time. We also release the annotation guidelines, the annotated data, and the parser model to the public.