Search Results for author:
Found 21 papers, 6 papers with code
Building Low-Resource NER Models Using Non-Speaker Annotations
In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages.
Ranked #1 on
Named Entity Recognition (NER)
on UNER v1 (Danish)
MasakhaNER: Named Entity Recognition for African Languages
We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders.
Simultaneous Translation and Paraphrase for Language Education
We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE).
Building Low-Resource NER Models Using Non-Speaker Annotation
In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Extending Multilingual BERT to Low-Resource Languages
Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning.
Cross-Lingual Ability of Multilingual BERT: An Empirical Study
Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no aligned data.
Robust Named Entity Recognition with Truecasing Pretraining
Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data.
Ranked #10 on
Named Entity Recognition (NER)
on WNUT 2017
Named Entity Recognition with Partially Annotated Training Data
Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated.
BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer
This paper describes the Cognitive Computation (CogComp) Group{'}s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Legal Linking: Citation Resolution and Suggestion in Constitutional Law
This paper describes a dataset and baseline systems for linking paragraphs from court cases to clauses or amendments in the US Constitution.
ner and pos when nothing is capitalized
While prior work and first impressions might suggest training a caseless model, or using a truecaser at test time, we show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text.
On the Strength of Character Language Models for Multilingual Named Entity Recognition
Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems.
Multilingual Named Entity Recognition
named-entity-recognition
+2
TALEN: Tool for Annotation of Low-resource ENtities
We present a new web-based interface, TALEN, designed for named entity annotation in low-resource settings where the annotators do not speak the language.
Simple Features for Strong Performance on Named Entity Recognition in Code-Switched Twitter Data
In this work, we address the problem of Named Entity Recognition (NER) in code-switched tweets as a part of the Workshop on Computational Approaches to Linguistic Code-switching (CALCS) at ACL{'}18.
Cheap Translation for Cross-Lingual Named Entity Recognition
Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e. g., the availability of Wikipedia in the target language.
Cross-lingual Dataless Classification for Languages with Small Wikipedia Presence
We use a word-level dictionary to convert documents in a SWL to a large-Wikipedia language (LWLs), and then perform CLDDC based on the LWL's Wikipedia.
Transliteration in Any Language with Surrogate Languages
We introduce a method for transliteration generation that can produce transliterations in every language.
ILLINOISCLOUDNLP: Text Analytics Services in the Cloud
Natural Language Processing (NLP) continues to grow in popularity in a range of research and commercial applications.
