3,186 research outputs found
FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection
Natural language processing (NLP) applications such as named entity
recognition (NER) for low-resource corpora do not benefit from recent advances
in the development of large language models (LLMs) where there is still a need
for larger annotated datasets. This research article introduces a methodology
for generating translated versions of annotated datasets through crosslingual
annotation projection. Leveraging a language agnostic BERT-based approach, it
is an efficient solution to increase low-resource corpora with few human
efforts and by only using already available open data resources. Quantitative
and qualitative evaluations are often lacking when it comes to evaluating the
quality and effectiveness of semi-automatic data generation strategies. The
evaluation of our crosslingual annotation projection approach showed both
effectiveness and high accuracy in the resulting dataset. As a practical
application of this methodology, we present the creation of French Annotated
Resource with Semantic Information for Medical Entities Detection (FRASIMED),
an annotated corpus comprising 2'051 synthetic clinical cases in French. The
corpus is now available for researchers and practitioners to develop and refine
French natural language processing (NLP) applications in the clinical field
(https://zenodo.org/record/8355629), making it the largest open annotated
corpus with linked medical concepts in French
Phonemic Segmentation and Labelling using the MAUS Technique
We describe the pronunciation model of the automatic segmentation technique MAUS based on a data-driven Markov process and a new evaluation measure for phonemic transcripts relative symmetric accuracy; results are given for the MAUS segmentation and labelling on German dialog speech. MAUS is currently distributed as a freeware package by the Bavarian Archive for Speech Signals and will also be implemented as a web-service in the near future
Detecting grammatical errors with treebank-induced, probabilistic parsers
Today's grammar checkers often use hand-crafted rule systems that define acceptable language. The development of such rule systems is labour-intensive and has to be repeated for each language. At the same time, grammars automatically induced from syntactically annotated corpora (treebanks) are successfully employed in other applications, for example text understanding and machine translation. At first glance, treebank-induced grammars seem to be unsuitable for grammar checking as they massively over-generate and fail to reject ungrammatical input due to their high robustness. We present three new methods for judging the grammaticality of a sentence with probabilistic, treebank-induced grammars, demonstrating that such grammars can be successfully applied to automatically judge the grammaticality of an input string. Our best-performing method exploits the differences between parse results for grammars trained on grammatical and ungrammatical treebanks. The second approach builds an estimator of the probability of the most likely parse using grammatical training data that has previously been parsed and annotated with parse probabilities. If the estimated probability of an input sentence (whose grammaticality is to be judged by the system) is higher by a certain amount than the actual parse probability, the sentence is flagged as ungrammatical. The third approach extracts discriminative parse tree fragments in the form of CFG rules from parsed grammatical and ungrammatical corpora and trains a binary classifier to distinguish grammatical from ungrammatical sentences. The three approaches are evaluated on a large test set of grammatical and ungrammatical sentences. The ungrammatical test set is generated automatically by inserting common grammatical errors into the British National Corpus. The results are compared to two traditional approaches, one that uses a hand-crafted, discriminative grammar, the XLE ParGram English LFG, and one based on part-of-speech n-grams. In addition, the baseline methods and the new methods are combined in a machine learning-based framework, yielding further improvements
Grammatical Error Correction: A Survey of the State of the Art
Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of
grammatical errors, such as missing prepositions and mismatched subject-verb
agreement, but also orthographic and semantic errors, such as misspellings and
word choice errors respectively. The field has seen significant progress in the
last decade, motivated in part by a series of five shared tasks, which drove
the development of rule-based methods, statistical classifiers, statistical
machine translation, and finally neural machine translation systems which
represent the current dominant state of the art. In this survey paper, we
condense the field into a single article and first outline some of the
linguistic challenges of the task, introduce the most popular datasets that are
available to researchers (for both English and other languages), and summarise
the various methods and techniques that have been developed with a particular
focus on artificial error generation. We next describe the many different
approaches to evaluation as well as concerns surrounding metric reliability,
especially in relation to subjective human judgements, before concluding with
an overview of recent progress and suggestions for future work and remaining
challenges. We hope that this survey will serve as comprehensive resource for
researchers who are new to the field or who want to be kept apprised of recent
developments
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