6,666 research outputs found

    Universal Dependencies for Learner English

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    We introduce the Treebank of Learner English (TLE), the first publicly available syntactic treebank for English as a Second Language (ESL). The TLE provides manually annotated POS tags and Universal Dependency (UD) trees for 5,124 sentences from the Cambridge First Certificate in English (FCE) corpus. The UD annotations are tied to a pre-existing error annotation of the FCE, whereby full syntactic analyses are provided for both the original and error corrected versions of each sentence. Further on, we delineate ESL annotation guidelines that allow for consistent syntactic treatment of ungrammatical English. Finally, we benchmark POS tagging and dependency parsing performance on the TLE dataset and measure the effect of grammatical errors on parsing accuracy. We envision the treebank to support a wide range of linguistic and computational research o n second language acquisition as well as automatic processing of ungrammatical language.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF – 1231216

    VALICO-UD: Treebanking an Italian Learner Corpus in Universal Dependencies

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    This article describes an ongoing project for the development of a novel Italian treebank in Universal Dependencies format: VALICO-UD. It consists of texts written by Italian L2 learners of different mother tongues (German, French, Spanish and English) drawn from VALICO, an Italian learner corpus elicited by comic strips. Aiming at building a parallel treebank currently missing for Italian L2, comparable with those exploited in Natural Language Processing tasks, we associated each learner sentence with a target hypothesis (i.e. a corrected version of the learner sentence written by an Italian native speaker), which is in turn annotated in Universal Dependencies. The treebank VALICO-UD is composed of 237 texts written by non-native speakers of Italian (2,234 sentences) and the related target hypotheses, all automatically annotated using UDPipe. A portion of this resource (36 texts corresponding to 398 learner sentences and related target hypotheses)—firstly released on May 2021 in the Universal Dependencies repository—is associated with error annotation and the automatic output is fully manually checked. In this article, we focus especially on the challenges addressed in treebanking a resource composed of learner texts. In addition, we report on a preliminary data exploration that makes use of three quantitative measures for assessing the quality of the data and for better understanding the role that this resource can play in tasks lying at the intersection of Computational Linguistics and learner corpus studies

    Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure

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    It has been established that incorporating word cluster features derived from large unlabeled corpora can significantly improve prediction of linguistic structure. While previous work has focused primarily on English, we extend these results to other languages along two dimensions. First, we show that these results hold true for a number of languages across families. Second, and more interestingly, we provide an algorithm for inducing cross-lingual clusters and we show that features derived from these clusters significantly improve the accuracy of cross-lingual structure prediction. Specifically, we show that by augmenting direct-transfer systems with cross-lingual cluster features, the relative error of delexicalized dependency parsers, trained on English treebanks and transferred to foreign languages, can be reduced by up to 13%. When applying the same method to direct transfer of named-entity recognizers, we observe relative improvements of up to 26%
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