Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer Reviewe
In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of i...
For the healthcare sector, it is critical to exploit the vast amount of textual health-related infor...
Background Text mining and natural language processing of clinical text, such as notes from electron...
Named Entity Recognition in the clinical domain and in languages different from English has the diff...
Medical texts such as radiology reports or electronic health records are a powerful source of data f...
This paper presents a proposal for wide--coverage Named Entity Recognition for Spanish. First, a lin...
This paper presents a proposal for wide--coverage Named Entity Recognition for Spanish. First, a lin...
This document presents both the working notes, neural-based model and conclusions regarding the deve...
Abstract. We present a named-entity recognition (NER) system for parallel multilingual text. Our sys...
We present the approach of the Turku NLP group to the PharmaCoNER task on Spanish biomedical named e...
The present work describes the proposed methods by the EdIE-KnowLab team in Information Extraction T...
We address the problem of recognition of medical entities in clinical records written in Italian. We...
This work studies Named Entity Recog-nition (NER) for Catalan without mak-ing use of annotated resou...
Biomedical Named Entity Recognition is a common task in Natural Language Processing applications, wh...
Referrals from the waiting list for several specialty consultations in Chilean public hospitals were...
In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of i...
For the healthcare sector, it is critical to exploit the vast amount of textual health-related infor...
Background Text mining and natural language processing of clinical text, such as notes from electron...
Named Entity Recognition in the clinical domain and in languages different from English has the diff...
Medical texts such as radiology reports or electronic health records are a powerful source of data f...
This paper presents a proposal for wide--coverage Named Entity Recognition for Spanish. First, a lin...
This paper presents a proposal for wide--coverage Named Entity Recognition for Spanish. First, a lin...
This document presents both the working notes, neural-based model and conclusions regarding the deve...
Abstract. We present a named-entity recognition (NER) system for parallel multilingual text. Our sys...
We present the approach of the Turku NLP group to the PharmaCoNER task on Spanish biomedical named e...
The present work describes the proposed methods by the EdIE-KnowLab team in Information Extraction T...
We address the problem of recognition of medical entities in clinical records written in Italian. We...
This work studies Named Entity Recog-nition (NER) for Catalan without mak-ing use of annotated resou...
Biomedical Named Entity Recognition is a common task in Natural Language Processing applications, wh...
Referrals from the waiting list for several specialty consultations in Chilean public hospitals were...
In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of i...
For the healthcare sector, it is critical to exploit the vast amount of textual health-related infor...
Background Text mining and natural language processing of clinical text, such as notes from electron...