Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCoDeRooMor: A new dataset for non-inflectional morphology studies of Swedish
CoderoMor: مجموعة بيانات جديدة لدراسات التشكل غير الانتهاء من السويدية
416
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تقدم الورقة موردا جديدا ورمائيا، لدراسة مورفولوجيا تكوين الكلمة السويدية الحديثة.تم تقسيم ما يقرب من 16.000 مادة معجمية في المورد بشكل يدويا إلى مورفيمز تشكيل Word، ومسمى لفئاتها، مثل البادئات والمعقاص والجذور، وما إلى ذلك آليات تكوين كلمة، مثل الاشتقاق والمضاعف قد ارتبطت بكل عنصرفي القائمة.توضح المقالة اختيار العناصر التوضيحي اليدوي ومبادئ التوضيحية، والتقارير المتعلقة بموثوقية التوضيحية اليدوية، ويعرض الأدوات والموارد وبعض الإحصاءات الأولى.بالنظر إلى طبيعة الموارد الخاصة بالموارد، من الممكن استخدامه للدراسات التجريبية وكذلك تطوير خوارزميات مدركة في مجال اللغويسيا تجزئة مورفيم ووضع العلامات (نهج الكلمات الفرعية الإحصائية CF).سيتم توفير المورد بحرية متاحة.
The paper introduces a new resource, CoDeRooMor, for studying the morphology of modern Swedish word formation. The approximately 16.000 lexical items in the resource have been manually segmented into word-formation morphemes, and labeled for their categories, such as prefixes, suffixes, roots, etc. Word-formation mechanisms, such as derivation and compounding have been associated with each item on the list. The article describes the selection of items for manual annotation and the principles of annotation, reports on the reliability of the manual annotation, and presents tools, resources and some first statistics. Given the''gold'' nature of the resource, it is possible to use it for empirical studies as well as to develop linguistically-aware algorithms for morpheme segmentation and labeling (cf statistical subword approach). The resource will be made freely available.
References used
https://aclanthology.org/
rate research
Read More
Automatic personalized corrective feedback can help language learners from different backgrounds better acquire a new language. This paper introduces a learner English dataset in which learner errors are accompanied by information about possible erro
r sources. This dataset contains manually annotated error causes for learner writing errors. These causes tie learner mistakes to structures from their first languages, when the rules in English and in the first language diverge. This new dataset will enable second language acquisition researchers to computationally analyze a large quantity of learner errors that are related to language transfer from the learners' first language. The dataset can also be applied in personalizing grammatical error correction systems according to the learners' first language and in providing feedback that is informed by the cause of an error.
We annotate 17,000 SNS posts with both the writer's subjective emotional intensity and the reader's objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer
and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer's subjective labels than the readers'. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.
The task of document-level text simplification is very similar to summarization with the additional difficulty of reducing complexity. We introduce a newly collected data set of German texts, collected from the Swiss news magazine 20 Minuten (20 Minu
tes') that consists of full articles paired with simplified summaries. Furthermore, we present experiments on automatic text simplification with the pretrained multilingual mBART and a modified version thereof that is more memory-friendly, using both our new data set and existing simplification corpora. Our modifications of mBART let us train at a lower memory cost without much loss in performance, in fact, the smaller mBART even improves over the standard model in a setting with multiple simplification levels.
We introduce the SweWinogender test set, a diagnostic dataset to measure gender bias in coreference resolution. It is modelled after the English Winogender benchmark, and is released with reference statistics on the distribution of men and women betw
een occupations and the association between gender and occupation in modern corpus material. The paper discusses the design and creation of the dataset, and presents a small investigation of the supplementary statistics.
In Romanian language there are some resources for automatic text comprehension, but for Emotion Detection, not lexicon-based, there are none. To cover this gap, we extracted data from Twitter and created the first dataset containing tweets annotated
with five types of emotions: joy, fear, sadness, anger and neutral, with the intent of being used for opinion mining and analysis tasks. In this article we present some features of our novel dataset, and create a benchmark to achieve the first supervised machine learning model for automatic Emotion Detection in Romanian short texts. We investigate the performance of four classical machine learning models: Multinomial Naive Bayes, Logistic Regression, Support Vector Classification and Linear Support Vector Classification. We also investigate more modern approaches like fastText, which makes use of subword information. Lastly, we fine-tune the Romanian BERT for text classification and our experiments show that the BERT-based model has the best performance for the task of Emotion Detection from Romanian tweets. Keywords: Emotion Detection, Twitter, Romanian, Supervised Machine Learning
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
