Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMultilingual Negation Scope Resolution for Clinical Text
قرار النفي متعدد اللغات للنص السريري
152
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
دقة النفي النفي هي مفتاح استخراج المعلومات عالية الجودة من النصوص السريرية، ولكن حتى الآن، كانت الجهود المبذولة لجعل المشفرين المستخدمة في نفي استخراج المعلومات، كانت تقتصر على اللغة الإنجليزية.نقدم نهجا عالميا لاستحقاق النرجب متعدد اللغات اللغوي، الذي يتغلب على عدم وجود بيانات تدريبية من خلال الاعتماد على الموارد المتفاوتة بلغات ومجالات مختلفة.نقيم نهجين للتعلم من هذه الموارد، والتدريب على البيانات والتدريب المجمع في إعداد التعلم متعدد المهام.تظهر تجاربنا أن دقة النطاق الصفرية في النص السريري ممكن، وأن الجمع بين الموارد المتاحة تعمل على تحسين الأداء في معظم الحالات.
Negation scope resolution is key to high-quality information extraction from clinical texts, but so far, efforts to make encoders used for information extraction negation-aware have been limited to English. We present a universal approach to multilingual negation scope resolution, that overcomes the lack of training data by relying on disparate resources in different languages and domains. We evaluate two approaches to learn from these resources, training on combined data and training in a multi-task learning setup. Our experiments show that zero-shot scope resolution in clinical text is possible, and that combining available resources improves performance in most cases.
References used
https://aclanthology.org/
rate research
Read More
India is one of the richest language hubs on the earth and is very diverse and multilingual. But apart from a few Indian languages, most of them are still considered to be resource poor. Since most of the NLP techniques either require linguistic know
ledge that can only be developed by experts and native speakers of that language or they require a lot of labelled data which is again expensive to generate, the task of text classification becomes challenging for most of the Indian languages. The main objective of this paper is to see how one can benefit from the lexical similarity found in Indian languages in a multilingual scenario. Can a classification model trained on one Indian language be reused for other Indian languages? So, we performed zero-shot text classification via exploiting lexical similarity and we observed that our model performs best in those cases where the vocabulary overlap between the language datasets is maximum. Our experiments also confirm that a single multilingual model trained via exploiting language relatedness outperforms the baselines by significant margins.
Transformer-based methods are appealing for multilingual text classification, but common research benchmarks like XNLI (Conneau et al., 2018) do not reflect the data availability and task variety of industry applications. We present an empirical comp
arison of transformer-based text classification models in a variety of practical monolingual and multilingual pretraining and fine-tuning settings. We evaluate these methods on two distinct tasks in five different languages. Departing from prior work, our results show that multilingual language models can outperform monolingual ones in some downstream tasks and target languages. We additionally show that practical modifications such as task- and domain-adaptive pretraining and data augmentation can improve classification performance without the need for additional labeled data.
The recent Text-to-Text Transfer Transformer'' (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 th
at was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent accidental translation'' in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generated AMRs, potentially limiting the transferability to the target task. I
n this paper, we investigate different techniques for automatically generating AMR annotations, where we aim to study which source of information yields better multilingual results. Our models trained on gold AMR with silver (machine translated) sentences outperform approaches which leverage generated silver AMR. We find that combining both complementary sources of information further improves multilingual AMR-to-text generation. Our models surpass the previous state of the art for German, Italian, Spanish, and Chinese by a large margin.
This paper introduces NorecNeg -- the first annotated dataset of negation for Norwegian. Negation cues and their in-sentence scopes have been annotated across more than 11K sentences spanning more than 400 documents for a subset of the Norwegian Revi
ew Corpus (NoReC). In addition to providing in-depth discussion of the annotation guidelines, we also present a first set of benchmark results based on a graph-parsing approach.
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
