scholarly journals RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System

2021 ◽  
Author(s):  
Haoyang Wen ◽  
Ying Lin ◽  
Tuan Lai ◽  
Xiaoman Pan ◽  
Sha Li ◽  
...  
Keyword(s):  
Information Extraction ◽  
Tracking System ◽  
Cross Media ◽  
Event Tracking ◽  
Cross Lingual

Eventman — A Business Event Tracking System

1994 ◽  
pp. 88-91
Author(s):  
Drago Čepar ◽  
Franci Mulec
Keyword(s):  
Tracking System ◽  
Event Tracking

An automated medical event tracking system

1998 ◽  
Vol 19 (3) ◽  
pp. S95
Author(s):  
Adam Payne ◽  
Scott Corley ◽  
Mary Mickel
Keyword(s):  
Tracking System ◽  
Medical Event ◽  
Event Tracking

scholarly journals Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction

2018 ◽  
Author(s):  
Hongyuan Mei ◽  
Sheng Zhang ◽  
Kevin Duh ◽  
Benjamin Van Durme
Keyword(s):  
Information Extraction ◽  
Cross Lingual

scholarly journals The Impact of Pretrained Language Models on Negation and Speculation Detection in Cross-Lingual Medical Text: Comparative Study

10.2196/18953 ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. e18953
Author(s):  
Renzo Rivera Zavala ◽  
Paloma Martinez
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Language Models ◽  
Special Focus ◽  
Rule Based ◽  
Clinical Narrative ◽  
Cross Lingual ◽  
The Impact

Background Negation and speculation are critical elements in natural language processing (NLP)-related tasks, such as information extraction, as these phenomena change the truth value of a proposition. In the clinical narrative that is informal, these linguistic facts are used extensively with the objective of indicating hypotheses, impressions, or negative findings. Previous state-of-the-art approaches addressed negation and speculation detection tasks using rule-based methods, but in the last few years, models based on machine learning and deep learning exploiting morphological, syntactic, and semantic features represented as spare and dense vectors have emerged. However, although such methods of named entity recognition (NER) employ a broad set of features, they are limited to existing pretrained models for a specific domain or language. Objective As a fundamental subsystem of any information extraction pipeline, a system for cross-lingual and domain-independent negation and speculation detection was introduced with special focus on the biomedical scientific literature and clinical narrative. In this work, detection of negation and speculation was considered as a sequence-labeling task where cues and the scopes of both phenomena are recognized as a sequence of nested labels recognized in a single step. Methods We proposed the following two approaches for negation and speculation detection: (1) bidirectional long short-term memory (Bi-LSTM) and conditional random field using character, word, and sense embeddings to deal with the extraction of semantic, syntactic, and contextual patterns and (2) bidirectional encoder representations for transformers (BERT) with fine tuning for NER. Results The approach was evaluated for English and Spanish languages on biomedical and review text, particularly with the BioScope corpus, IULA corpus, and SFU Spanish Review corpus, with F-measures of 86.6%, 85.0%, and 88.1%, respectively, for NeuroNER and 86.4%, 80.8%, and 91.7%, respectively, for BERT. Conclusions These results show that these architectures perform considerably better than the previous rule-based and conventional machine learning–based systems. Moreover, our analysis results show that pretrained word embedding and particularly contextualized embedding for biomedical corpora help to understand complexities inherent to biomedical text.

Technological University and Its Affiliated Engineering Colleges Event Tracking System Using Blockchain Technology

2020 ◽  
Vol 17 (9) ◽  
pp. 4493-4499
Author(s):  
M. Niranjanamurthy ◽  
N. M. Niveditha ◽  
M. P. Amulya ◽  
A. R. Namitha
Keyword(s):  
Web Applications ◽  
Tracking System ◽  
Complete Information ◽  
Colleges And Universities ◽  
Blockchain Technology ◽  
Event Tracking ◽  
Technological University

Now a day’s people getting all information’s through web applications. The objective of this work is to develop a system that will provide the details of Engineering colleges and Universities in Karnataka and the complete information of all the events happening in different colleges. Events like conference, fest etc. All the details will be provided in this system. In TUECETS Users or visitors can easily get to know currently happening events and events which are to be held later. Universities and affiliated Engineering colleges administrator can post the details of the events in this system through BC. Administrator will be responsible to maintain all the posts and events details. Events can be posted in the system only after the approval of the administrator through Blockchain. It will be very useful to all users to know the events and activities details of particular university.

CrossOIE: Cross-Lingual Classifier for Open Information Extraction

2020 ◽  
pp. 368-378
Author(s):  
Bruno Souza Cabral ◽  
Rafael Glauber ◽  
Marlo Souza ◽  
Daniela Barreiro Claro
Keyword(s):  
Information Extraction ◽  
Open Information Extraction ◽  
Cross Lingual

scholarly journals Cross-lingual information extraction system evaluation

2004 ◽  
Author(s):  
Kiyoshi Sudo ◽  
Satoshi Sekine ◽  
Ralph Grishman
Keyword(s):  
Information Extraction ◽  
Extraction System ◽  
System Evaluation ◽  
Cross Lingual ◽  
Information Extraction System

scholarly journals The Impact of Pretrained Language Models on Negation and Speculation Detection in Cross-Lingual Medical Text: Comparative Study (Preprint)

2020 ◽  
Author(s):  
Renzo Rivera Zavala ◽  
Paloma Martinez
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Language Models ◽  
Special Focus ◽  
Rule Based ◽  
Clinical Narrative ◽  
Cross Lingual ◽  
The Impact

BACKGROUND Negation and speculation are critical elements in natural language processing (NLP)-related tasks, such as information extraction, as these phenomena change the truth value of a proposition. In the clinical narrative that is informal, these linguistic facts are used extensively with the objective of indicating hypotheses, impressions, or negative findings. Previous state-of-the-art approaches addressed negation and speculation detection tasks using rule-based methods, but in the last few years, models based on machine learning and deep learning exploiting morphological, syntactic, and semantic features represented as spare and dense vectors have emerged. However, although such methods of named entity recognition (NER) employ a broad set of features, they are limited to existing pretrained models for a specific domain or language. OBJECTIVE As a fundamental subsystem of any information extraction pipeline, a system for cross-lingual and domain-independent negation and speculation detection was introduced with special focus on the biomedical scientific literature and clinical narrative. In this work, detection of negation and speculation was considered as a sequence-labeling task where cues and the scopes of both phenomena are recognized as a sequence of nested labels recognized in a single step. METHODS We proposed the following two approaches for negation and speculation detection: (1) bidirectional long short-term memory (Bi-LSTM) and conditional random field using character, word, and sense embeddings to deal with the extraction of semantic, syntactic, and contextual patterns and (2) bidirectional encoder representations for transformers (BERT) with fine tuning for NER. RESULTS The approach was evaluated for English and Spanish languages on biomedical and review text, particularly with the BioScope corpus, IULA corpus, and SFU Spanish Review corpus, with F-measures of 86.6%, 85.0%, and 88.1%, respectively, for NeuroNER and 86.4%, 80.8%, and 91.7%, respectively, for BERT. CONCLUSIONS These results show that these architectures perform considerably better than the previous rule-based and conventional machine learning–based systems. Moreover, our analysis results show that pretrained word embedding and particularly contextualized embedding for biomedical corpora help to understand complexities inherent to biomedical text.

Sign in / Sign up

Export Citation Format

Share Document