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Extending a CRF-based named entity recognition model for Turkish well formed text and user generated content1
2017
Semantic Web Journal
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This article presents the enhancements made to a Turkish named entity recognition model [5] (based on conditional random fields (CRFs) and originally tailored for well formed texts) in order to extend ...
its covered named entity types, and also to process extra challenging user generated content coming with Web 2.0. ...
Acknowledgements We would like to acknowledge that this work is part of a research project entitled "Parsing Web 2.0 Sentences" subsidized by the TUBITAK (Turkish Scienti fic and Technological Research ...
doi:10.3233/sw-170253
fatcat:6e47nqhzyjabrhl6t7cgijm7xm
Mining social semantics on the social web
2017
Semantic Web Journal
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-Extending a CRF-based Named Entity Recognition Model for Turkish Well Formed Text and User Generated Content by GökhanŞeker and Gülşen Eryigit: The detection of named entities is still a major challenge ...
For example, content from the social web could be enriched and linked to the semantic web using named entity recognition and linking, as well as sentiment analysis. ...
doi:10.3233/sw-170272
fatcat:tn4i46l43faixbxa6q7zkpa3la
An evaluation of recent neural sequence tagging models in Turkish named entity recognition
2021
Expert systems with applications
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Named entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. ...
Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art ...
Acknowledgement Authors would like to thank Kemal Oflazer, Onur Güngör and Tunga Güngör for their assistance in obtaining the Turkish NER dataset. ...
doi:10.1016/j.eswa.2021.115049
fatcat:mpachywctzbhtkuwkwy7hovt5y
An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition
[article]
2020
arXiv
pre-print
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Named entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. ...
Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art ...
Recently, neural models have been introduced to named entity task in well-formed and noisy texts (Al-Nabki et al., 2020) . ...
arXiv:2005.07692v2
fatcat:ongxceoqijbu7g4xjkb545dapm
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings
[article]
2018
arXiv
pre-print
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In this study, we focused on the Named Entity Recognition (NER) problem on informal text types for Turkish. We utilized a semi-supervised learning approach based on neural networks. ...
We made use of these obtained word embeddings, together with language independent features that are engineered to work better on informal text types, for generating a Turkish NER system on microblog texts ...
We would also like to thank The Scientific and Technological Research Council of Turkey (TÜBİTAK), The Science Fellowships and Grant Programmes Department (BİDEB) for providing financial support with 2210 ...
arXiv:1810.08732v1
fatcat:c5hvilxj7zaejf7raeuz2kc534
Using Local Grammar for Entity Extraction from Clinical Reports
2015
International Journal of Interactive Multimedia and Artificial Intelligence
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Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyze texts written in natural language to extract structured and useful information such as named entities and ...
Hence, a system for extracting this information in a structured form can benefits healthcare professionals. ...
of an ensemble learning-based approach and a Vector Space Model based method for disorder entity encoding. ...
doi:10.9781/ijimai.2015.332
fatcat:i7q5tilqk5cozds5vz4b4eyqgq
Extracting Temporal Entity from Urdu Language Text
2020
University of Sindh Journal of Information and Communication Technology
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In this paper, we propose a rule-based approach for temporal entity extraction for Urdu language. ...
However, research for Urdu language lags far behind and there is a need for lot of work to be done in this regard especially when huge quantity of Urdu data is being generated on online social networks ...
A sequencer system developed for analysis of temporal entities [21] existing in news articles and user generated unstructured contents. ...
doaj:388be7e81ab64e0c9cc849d17b0d6820
fatcat:hbgyud3nwbfu7k6dlt2s42fyo4
A hybrid model of sentimental entity recognition on mobile social media
2016
EURASIP Journal on Wireless Communications and Networking
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In this paper, a hybrid sentimental entity recognition model (HSERM) has been designed. ...
It provides us a data source that we can use to extract peoples' opinions which are important for product review and public opinion monitoring. ...
Finally, combining some rules, the names could be recognized automatically. Turkish scholars [12] did the named-entity recognition on their domestic twitter. ...
doi:10.1186/s13638-016-0745-7
fatcat:kvghtwvi3bdltpkeiuwckkl2ry
A Survey of Code-switched Speech and Language Processing
[article]
2020
arXiv
pre-print
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We motivate why processing code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. ...
This survey reviews computational approaches for code-switched Speech and Natural Language Processing. ...
Named Entity Recognition (NER) Named Entity Recognition (NER) datasets for code-switching are similar to LID datasets, with word-level annotations. • A shared task was organized to address NER for code-switched ...
arXiv:1904.00784v3
fatcat:r5tsg4kdnfbtnndae523c32pta
Extraction and Analysis of Social Networks Data to Detect Traffic Accidents
2022
Information
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The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. ...
Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. ...
Furthermore, a thank you to the Programming Languages and Systems (PLaS) research group for their feedback on the work. ...
doi:10.3390/info13010026
fatcat:nexem2bl4vcm3evk5thtzjl4we
WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans
[article]
2021
arXiv
pre-print
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In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. ...
Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts. ...
Acknowledgments We would like to thank the shared task organizers for making this interesting dataset available. We further thank the anonymous SemEval reviewers for their insightful feedback. ...
arXiv:2104.04630v3
fatcat:2uqhyrjudbbr5jwgnljqnieslu
ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer
[article]
2022
arXiv
pre-print
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Named entity recognition enables the identification of proper names as well as temporal and numeric expressions in an open domain text. ...
In this paper, we present an Amharic named entity recognition system based on bidirectional long short-term memory with a conditional random fields layer. ...
On the other hand, rule-based systems generally perform quite well in the identification of temporal and numerical expressions. ...
arXiv:2207.00785v1
fatcat:z76zwhd25fazjnybuftvl3paii
Towards Generalizable Place Name Recognition Systems
2018
Proceedings of the 12th Workshop on Geographic Information Retrieval - GIR'18
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Most of the previous work done on entity recognition for English makes use of similar corpora for both training and testing. ...
Place name recognition is one of the key tasks in Information Extraction. In this paper, we tackle this task in English News from India. ...
ACKNOWLEDGEMENTS We thank our team members for their feedbacks and suggestions regarding this paper. ...
doi:10.1145/3281354.3281363
dblp:conf/gis/AkdemirHYGYY18
fatcat:7abmfcqofnck5kq7gtuxxzngvi
Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis
2020
IEEE Access
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NLP-based [92, 93] exploits NLP-based resources mainly named entity recognition that includes a stop words list, linguistic knowledge base, sentiment lexicons, and general-purpose natural language processing ...
Generally, SA is being investigated based on three ranks, namely: document, sentence, and aspect [1] . ...
Apart from that, he also actively appeared in the mass media, a writer for local newspapers and an invited speaker on political, social, economic and current issues in the country. ...
doi:10.1109/access.2020.3031217
fatcat:vosmjncbe5h6lfaoucmjy2yxq4
Challenges, Techniques, and Trends of Simple Knowledge Graph Question Answering: A Survey
2021
Information
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Simple questions are the most common type of questions used for evaluating a knowledge graph question answering (KGQA). ...
In this paper, we present a comprehensive survey of answering simple questions to classify available techniques and compare their advantages and drawbacks in order to have better insights of existing issues ...
Task
Caption
Input
Output
Used in
Named entity recognition
To classify tokens accord-
ing to a class
Text
NER
Entity and relation linking
Extractive question answer-
ing
To extract an answer ...
doi:10.3390/info12070271
fatcat:so5vmq7pkbdj3alkfnxf6xjchq
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