Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCross-Lingual Named Entity Recognition via FastAlign: a Case Study
التعرف على الكيان المسمى عبر اللغات عبر Fastalign: دراسة حالة
307
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يعد التعرف على الكيان المسمى مهمة أساسية في معالجة اللغة الطبيعية للكشف عن الكيانات وتصنيفها إلى فئات محددة سلفا. كيان هو كلمة ذات معنى، أو عبارة تشير إلى الأسماء المناسبة. تلعب الكيانات المسماة دورا مهما في مهام NLP المختلفة مثل استخراج المعلومات، والرد على الأسئلة والترجمة الآلية. في الترجمة الآلية، غالبا ما تسبب الكيانات المسماة فشل الترجمة بغض النظر عن السياق المحلي، مما يؤثر على جودة الإخراج للترجمة. التعليق الكيانات المسماة هي عملية تستغرق وقتا طويلا ومكلفة خاصة لغات الموارد المنخفضة. أحد الحلول لهذه المشكلة هو استخدام طرق محاذاة Word في شركة موازية ثنائية اللغة تم تفاحها جانب واحد فقط. الهدف هو استخراج الكيانات المسماة في اللغة المستهدفة باستخدام Corpus المشرف من لغة المصدر. في هذه الورقة، قارنا أداء اثنين من أساليب المحاذاة، واللبسائع التناظرية المتناثرة-Diag-Final-Final وتقاطع التناظر، لاستغلال الإسقاط التوضيحي للبرتغالية البرتغالية الإنجليزية البرازيلية للكشف عن الكيانات المسماة في البرتغالية البرازيلية. يتم استخدام نموذج NER الذي تم تدريبه على البيانات المشروحة المستخرجة من أساليب المحاذاة، لتقييم أداء المحاذاين. تظهر النتائج التجريبية أن التعايدة بين التقاطعات قادرة على تحقيق درجات أداء فائقة مقارنة مع النمو - النهائي والنهائي والمشي في البرتغالية البرازيلية.
Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categories. An entity is a meaningful word, or phrase that refers to proper nouns. Named Entities play an important role in different NLP tasks such as Information Extraction, Question Answering and Machine Translation. In Machine Translation, named entities often cause translation failures regardless of local context, affecting the output quality of translation. Annotating named entities is a time-consuming and expensive process especially for low-resource languages. One solution for this problem is to use word alignment methods in bilingual parallel corpora in which just one side has been annotated. The goal is to extract named entities in the target language by using the annotated corpus of the source language. In this paper, we compare the performance of two alignment methods, Grow-diag-final-and and Intersect Symmetrisation heuristics, to exploit the annotation projection of English-Brazilian Portuguese bilingual corpus to detect named entities in Brazilian Portuguese. A NER model that is trained on annotated data extracted from the alignment methods, is used to evaluate the performance of aligners. Experimental results show the Intersect Symmetrisation is able to achieve superior performance scores compared to the Grow-diag-final-and heuristic in Brazilian Portuguese.
References used
https://aclanthology.org/
rate research
Read More
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limite
d. In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.
Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition
(NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1
Abstract We study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity Ratio, to learn models in the presence of syste
matically missing tags. We show that our approach is both theoretically sound and empirically useful. Experimentally, we find that it meets or exceeds performance of strong and state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. In particular, we find that it significantly outperforms the previous state-of-the-art methods from Mayhew et al. (2019) and Li et al. (2021) by +12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased annotations, averaged across 7 datasets. We also show that, when combined with our approach, a novel sparse annotation scheme outperforms exhaustive annotation for modest annotation budgets.1
We explore the application of state-of-the-art NER algorithms to ASR-generated call center transcripts. Previous work in this domain focused on the use of a BiLSTM-CRF model which relied on Flair embeddings; however, such a model is unwieldy in terms
of latency and memory consumption. In a production environment, end users require low-latency models which can be readily integrated into existing pipelines. To that end, we present two different models which can be utilized based on the latency and accuracy requirements of the user. First, we propose a set of models which utilize state-of-the-art Transformer language models (RoBERTa) to develop a high-accuracy NER system trained on custom annotated set of call center transcripts. We then use our best-performing Transformer-based model to label a large number of transcripts, which we use to pretrain a BiLSTM-CRF model and further fine-tune on our annotated dataset. We show that this model, while not as accurate as its Transformer-based counterpart, is highly effective in identifying items which require redaction for privacy law compliance. Further, we propose a new general annotation scheme for NER in the call-center environment.
To address a looming crisis of unreproducible evaluation for named entity recognition, we propose guidelines and introduce SeqScore, a software package to improve reproducibility. The guidelines we propose are extremely simple and center around trans
parency regarding how chunks are encoded and scored. We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and statistically significant. We describe SeqScore, which addresses many of the issues that cause replication failures.
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
