Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Jannik Strötgen"'
Publikováno v:
Proceedings of the 7th Workshop on Representation Learning for NLP.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dae5c503ef4396ab2e371c02ebfa836a
https://doi.org/10.18653/v1/2022.repl4nlp-1.19
https://doi.org/10.18653/v1/2022.repl4nlp-1.19
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030721121
ECIR (1)
ECIR (1)
With the aim of facilitating internal processes as well as search applications, patent offices categorize documents into taxonomies such as the Cooperative Patent Categorization. This task corresponds to a multi-label hierarchical text classification
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bff8a6d51129eb397f75d72372c3e49
https://doi.org/10.1007/978-3-030-72113-8_34
https://doi.org/10.1007/978-3-030-72113-8_34
The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improvements in benchmark datasets for various tasks, these models often perf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f25f7b8ec6094e2ccee06c4db2465c12
Publikováno v:
BioNLP-OST@EMNLP-IJNCLP
Named entity recognition has been extensively studied on English news texts. However, the transfer to other domains and languages is still a challenging problem. In this paper, we describe the system with which we participated in the first subtrack o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1009d199586e2467809ec4b41b9b717
http://arxiv.org/abs/2007.01022
http://arxiv.org/abs/2007.01022
Publikováno v:
ACL
Exploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8d97d34d8f901266d3974670ace1a91
http://arxiv.org/abs/2005.09397
http://arxiv.org/abs/2005.09397
Publikováno v:
WWW
Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering. As KGs are often automatically constructed, they may contain incorrect facts. Detecting them is a crucial, yet extremely expensive task.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd8ffcd2b7349b7195bcb78acf990f39
https://doi.org/10.1145/3366423.3380014
https://doi.org/10.1145/3366423.3380014
Publikováno v:
NAACL-HLT
Deep neural networks and huge language models are becoming omnipresent in natural language applications. As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::167b307daeea6856ab801d42104f974d
Publikováno v:
RepL4NLP@ACL
Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models. In this analysis paper, we investigate whether the best auxiliary language can be predicted based on language dist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38974dd386bb37fe040af205a25364e1
Publikováno v:
RepL4NLP@ACL
Although temporal tagging is still dominated by rule-based systems, there have been recent attempts at neural temporal taggers. However, all of them focus on monolingual settings. In this paper, we explore multilingual methods for the extraction of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d97295da72e0c52347b45034aea3bcb8