A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Filters
Role-based model for Named Entity Recognition
2017
RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning
Preserved Fulltext
Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. ...
This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. ...
This task aims to create the role classification model based on the named entity type. ...
doi:10.26615/978-954-452-049-6_021
dblp:conf/ranlp/CallejaGCG17
fatcat:yofszhy4sncl5k27254yqrrjfy
Overview of Named Entity Recognition
2022
Journal of Contemporary Educational Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This paper discusses the existing named entity recognition technology based on its history of development. ...
Named entity recognition, as a sub-task of information extraction, has attracted widespread attention from scholars at home and abroad since it was proposed, and a series of studies and discussions have ...
At present, the named entity recognition technology for general texts has been established, and many researchers are focusing on the use of deep learning and attention mechanism for named entity recognition ...
doi:10.26689/jcer.v6i5.3958
fatcat:g4f7qga6wvd4zonfjreg4oqv64
Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information
2022
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Compared with the baseline model on Conll04, EINET obtains improvements by 2.37% in F1 for named entity recognition and 3.43% in F1 for relation extraction. ...
First, on the premise of using the pre-trained model, we introduce explicit semantics from Semantic Role Labeling (SRL), which contains rich semantic features about the entity types and relation of entities ...
Named Entity Recognition: Named Entity Recognition is mainly responsible for obtaining candidate entity representations by span-based methods. ...
doi:10.3390/app12126231
fatcat:676fju57xvbj3oefmxlijrdbni
A French Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News
2020
International Conference on Language Resources and Evaluation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The presented corpus consists of financial news articles in French and allows for training and evaluating domain-specific named entity recognition and relation extraction algorithms. ...
We present some of our experimental results on named entity recognition and relation extraction using our annotated corpus. ...
This ontology serves as a model for custom named entity recognition and relation extraction tasks and will eventually be used as the schema of a future knowledge base of financial relations (Jabbari et ...
dblp:conf/lrec/JabbariSZC20
fatcat:jmhqu2q3t5e65acypwgrbgyq7e
Poincaré Embeddings in the Task of Named Entity Recognition
[chapter]
2020
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a classifier model for the NER (Named Entity Recognition) task by implementing Poincaré embeddings and by using the most frequent n-grams and their Part-of-Speech (POS) structures ...
We found that POS structures and n-grams help to map possible named entities, while using Poincaré embeddings manage to affirm and refine this recognition, improving the recognition of named entities. ...
In summary, this paper presents a classifier model for the task of named entity recognition by employing Poincaré embeddings, n-grams and Part-of-Speech. ...
doi:10.1007/978-3-030-60887-3_17
fatcat:ea7q7mcakrhd7f6gtlm2hckg5i
Large Language Models for Latvian Named Entity Recognition
2020
Human Language Technology - The Baltic Perspectiv
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Transformer-based language models pre-trained on large corpora have demonstrated good results on multiple natural language processing tasks for widely used languages including named entity recognition ...
In this paper, we investigate the role of the BERT models in the NER task for Latvian. We introduce the BERT model pre-trained on the Latvian language data. ...
Acknowledgements This research has been supported by the European Regional Development Fund within the joint project of SIA TILDE and University of Latvia "Multilingual Artificial Intelligence Based Human ...
doi:10.3233/faia200603
dblp:conf/hlt/ViksnaS20
fatcat:ppejs74ihnb3njupofnzsz7vq4
Sentiment processing of social media information from both wireless and wired network
2016
EURASIP Journal on Wireless Communications and Networking
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
During the process, name entities with the same meaning are clustered and sentiment carrier is filtered, with which sentiment can be got even users express their feeling for the same object with different ...
Research work focuses on analyzing sentiment orientation for specific aspects of product with explicit names. ...
At last, patterns of roles are set for person name recognition. ...
doi:10.1186/s13638-016-0661-x
fatcat:mu2wjxmo6jg7rmzvnrwktdu63a
Automatic Recognition of Chinese Location Entity
[chapter]
2014
Communications in Computer and Information Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Recognition of Chinese location entity is an important part of event extraction. In this paper we propose a novel method to identify Chinese location entity based on the divide-and-conquer strategy. ...
Firstly, we use CRF role labeling to identify the basic place name. Secondly, by using semi-automatic way, we build indicator lexicon. ...
Location Entity Recognition As discussed before, we use the model of location entity recognition to identify location entity. The definition of the model is as follows. ...
doi:10.1007/978-3-662-45924-9_34
fatcat:eimbjbpxvjfpjkni6xrnx5o2qq
NiuParser: A Chinese Syntactic and Semantic Parsing Toolkit
2015
Proceedings of ACL-IJCNLP 2015 System Demonstrations
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
It can handle a wide range of Natural Language Processing (NLP) tasks in Chinese, including word segmentation, partof-speech tagging, named entity recognition, chunking, constituent parsing, dependency ...
parsing, and semantic role labeling. ...
Named Entity Recognition In academics, named entity recognition often suffers from limited training data. ...
doi:10.3115/v1/p15-4025
dblp:conf/acl/ZhuZWX15
fatcat:unpery7hw5cqxjrar5rzagjk5e
Medical Named Entity Recognition Based on Overlapping Neural Networks
2020
Procedia Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Aiming at the existing problems, we put forward a overlapping neural network for medical named entity recognition. ...
Aiming at the existing problems, we put forward a overlapping neural network for medical named entity recognition. ...
For named entity recognition in the medical field, Wang et al. ...
doi:10.1016/j.procs.2020.06.052
fatcat:nfh7e2pxbnhzldb4u2tpy6flzu
Named Entity Recognition in Biomedical Domain: A Survey
2019
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Named Entity Recognition plays an important role in locating and classifying atomic elements into predefined categories such as person names, locations, organizations, expression of times, temporal expressions ...
Named Entity Recognition (NER) is one of the major tasks in Natural Language Processing (NLP). NER has been an active area of research for the past twenty years. ...
CONCLUSION This paper provides a review of Named Entity Recognition methods in the biomedical field. ...
doi:10.5120/ijca2019918469
fatcat:n2cumq3lpjgqblf64otnoxal64
Name Entity Recognition by New Framework Using Machine Learning Algorithm
2014
IOSR Journal of Computer Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
These names are called Named Entities (NE) and Named Entity Recognition (NER), one of the main tasks of IE systems, seeks to locate and classify automatically these names into predefined categories. ...
Our approach makes use of English contextual and morphological information to extract named entities. The context is represented by means of words that are used as clues for each named entity type. ...
about Hidden Markov Model (HMM) and the Gazetteer method for name entity recognition. ...
doi:10.9790/0661-16546671
fatcat:z2if4lmy6val3f7d4q6fin32s4
Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
To train our datasets for named entity recognition, we utilized Spacey's custom NER model, Amazon Comprehend Custom NER. ...
Our model has an accuracy of 87.99% in Spacy Custom Named Entity recognition, 95.51% in Amazon Comprehend Custom NER, and 87.02% in the Sequential model for demand analysis. ...
Through the name entity recognition model we successfully identified the gender of the person based on their names. ...
arXiv:2204.01827v1
fatcat:u4h62fukv5b3znoqbnkcfis5x4
Recognizing Names in Islam-Related Russian Twitter
2017
International Conference on Data Analytics and Management in Data Intensive Domains
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Two-stage approaches to named entity recognition and Word2vec-based clustering were also useful for our task. ...
Specific difficulties of our collection for named entity recognition include a large number of Arabic and other Eastern names and frequent use of ALL-CAPS spelling for emphasizing main words in messages ...
The authors used two approaches for the named entity recognition: knowledge-based and CRF-based approach. ...
dblp:conf/rcdl/MozharovaL17
fatcat:mnvqf5la25dshggc32kyhqxbuq
Feature-Rich Twitter Named Entity Recognition and Classification
2016
Workshop on Noisy User-generated Text
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
For Twitter named entity recognition on unseen test data, our system obtained the second highest F 1 score in the shared task: 63.22%. ...
Twitter named entity recognition is the process of identifying proper names and classifying them into some predefined labels/categories. ...
Babelfy named entities: Each tweet was passed to the Babelfy (Moro et al., 2014 ) named entity recognition system for recognizing Twitter names. ...
dblp:conf/aclnut/SikdarG16
fatcat:cmu34hfsjvdctlsfi3latzvesm
« Previous
Showing results 1 — 15 out of 213,302 results