NLPR-SI 2021 : Special Issue on Information Extraction and NLP from the Natural Language Processing Research Journal
Call For Papers
Please see this CfP of the Special Issue on Information Extraction and NLP from the Natural Language Processing Research Journal: https://www.atlantis-press.com/journals/nlpr/news. Or a summary below:
Aims and Scope
With the growth of big data, we are confronted with the pressing needs of automated means to support the mining and sense-making of very large amount of data. One of the keys to unlocking values in such big data is to be able to interpret human natural language, extract information from unstructured text, and represent them in a machine understandable format. The success of these activities requires the development of Natural Language Processing (NLP) and Information Extraction (IE) methods that power a wide range of technologies we experience on a daily basis, such as search engines, knowledge graphs, and smart assistants. They are also crucial to a wide range of disciplines, such as information retrieval, data fusion, and the Semantic Web.
For these reasons, in recent years, NLP and IE have seen fast-growing interest and unprecedented opportunities in both research and practice. Some of these highly recognised efforts include the IBM Watson Natural Language Understanding engine, the Never-Ending Language Learning (NELL) project led by the Carnegie Mellon University, the knowledge graph projects that power Google and Bing search engines, Baidu BROAD, Amazon Comprehend, and Google’s Bidirectional Encoder Representations from Transformers (BERT) model that marks a milestone in NLP. The significant advance in the NLP and IE fields has brought significant opportunities, but also opened up new challenges for research. Some of the questions that remain to be answered include: beyond the recent language models such as GPT-3 and those BERT-based, what are the directions for algorithmic research? How do such language models impact on IE methods? How well do they adapt to domain-specific tasks and industry context? And generally, what are the lessons we have learned from the past decade of advance in research and where should NLP and IE research go?
This special issue sets up a timely effort to address some of these questions by inviting scholarly contributions covering the recent advance in NLP and IE. Papers submitted to this special issue should address tasks that directly tackle, or have a clear link to, the extraction of structured information from human natural language texts (either structured or unstructured). We welcome original research articles reporting development of novel methods and algorithms, as well as literature survey papers that summarising a key subject area.
Main topics and quality control
This special issue welcomes submissions covering a wide range of topic areas such as those listed below.
• Named entity recognition and linking
• Relation extraction and classification
• Terminology extraction and classification
• Template filling (e.g., event extraction)
• Knowledge base/graph construction and alignment
• NLP/IE from semi-structured content, e.g., wrapper induction, table mining
• NLP/IE applications to problems in another subject field, e.g., Information Retrieval, Semantic Web, Social Media, information and knowledge integration
• NLP/IE applications to industry/domain specific context
• Interpretability and Analysis of Models for NLP/IE
• Machine Learning for NLP/IE
• Resources and Evaluation
• Semantics: Lexical, Sentence level, Textual Inference and Other areas
• Sentiment Analysis
• Argument Mining
• Representation learning for NLP/IE tasks
• (Information) Nutrition labels for the Web
• Negative results: what lessons can be learned to inform future research
Because of the wide scope of NLP and IE, it is possible that some important topics that fit the merit of this special issue are not listed above. Therefore, if you are unsure whether your work would fit, we encourage to get in touch with the correspondence editor with the email above.
All papers must comply with the basic requirements of the NLPR journal (see below), and will be subject to a peer-review process.
Submission of papers: 30, April2021
Notification of review results: 31 May, 2021
Submission of revised papers: 31 July, 2021
Notification of final review results: 31 August, 2021
Submit your paper
All papers have to be submitted via the Editorial Manager online submission and peer review system. Instructions will be provided on screen and you will be stepwise guided through the process of uploading all the relevant article details and files associated with your submission. All manuscripts must be in the English language.
To access the online submission site for the journal, please visit https://www.editorialmanager.com/nlpr/default.aspx. Note that if this is the first time that you submit to the Natural Language Processing Research, you need to register as a user of the system first.