Special Issue "Information Extraction and Language Discourse Processing"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2023 | Viewed by 2379

Special Issue Editors

TIB–Leibniz Information Centre for Science and Technology, 30167 Hannover, Germany
Interests: information extraction; text mining; natural language processing; knowledge graphs
Professor, School of Economics and Management, Nanjing University of Science and Technology (NJUST), No. 200, Xiaolingwei, 210094 Nanjing, China
Interests: scientific text mining; knowledge entity extraction and evaluation; social media mining

Special Issue Information

Dear Colleagues,

Information extraction (IE) plays an increasingly important and pervasive role in today’s era of digitalized communication media based on the Semantic Web. E.g., search engine results, as snippets, are slowly replaced by “rich snippets”; there is an interest in converting scholarly publications to structured records available in such downstream IT applications as Leaderboards, etc. IE is thus the task of automatically extracting structured information from unstructured and/or semi-structured electronically represented documents. In most cases, this activity concerns processing of human language texts by means of natural language processing (NLP). The automatic extraction of information from unstructured sources has opened up new avenues for querying, organizing, and analyzing data by drawing upon the clean semantics of structured databases and the abundance of unstructured data.

Apart from extrinsic models of IE, research in linguistics and computational linguistics have long pointed out that text is not just simple sequence of clauses and sentences but rather follows a highly elaborated structure formalized within discourse. The framework used for discourse analysis has long since been rhetorical structure theory (RST). Within a well-written text, no unit of the text is completely isolated; interpretation requires understanding the unit’s relation with the context. Research in discourse analysis aims to unmask such relations in the text, which is helpful for many downstream applications such as summarization, information retrieval, and question answering.

This Special Issue seeks novel research reports on the spectrum that blends information extraction and language discourse processing research in diverse communities. The editors welcome submissions along various dimensions derived from the nature of the extraction task, the advanced neural techniques used for extraction, the variety of input resources exploited, and the type of output produced. Quantitative, qualitative, and mixed methods studies are welcome, as are case studies and experience reports if they describe an impactful application at a scale that delivers useful lessons to the journal readership.

Topics of interest include (but are not limited to):

  • Knowledge base population with discourse-centric information extraction (IE)
  • Coreference resolution and its impact on discourse-centric IE
  • Relationship extraction leveraging linguistic discourse
  • Template filling
  • Impact of pragmatics or rhetorics on information extraction
  • Discourse-centric IE at scale
  • Intelligent and novel assessment models of discourse-centric IE
  • Survey of discourse-centric IE in natural language processing (NLP)
  • Challenges implementing discourse-centric IE in real-world scenarios
  • Modeling domains using discourse-centric IE
  • Human–AI hybrid systems for learning discourse and IE
  • Application of discourse-centric IE

Dr. Jennifer D'Souza
Prof. Dr. Chengzhi Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • coherence
  • topic focus
  • information structure
  • conversation structure
  • discourse processing
  • scholarly discourse processing
  • anaphora resolution

Published Papers (1 paper)

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Extracting Narrative Patterns in Different Textual Genres: A Multilevel Feature Discourse Analysis
Information 2023, 14(1), 28; https://doi.org/10.3390/info14010028 - 31 Dec 2022
Viewed by 1182
We present a data-driven approach to discover and extract patterns in textual genres with the aim of identifying whether there is an interesting variation of linguistic features among different narrative genres depending on their respective communicative purposes. We want to achieve this goal [...] Read more.
We present a data-driven approach to discover and extract patterns in textual genres with the aim of identifying whether there is an interesting variation of linguistic features among different narrative genres depending on their respective communicative purposes. We want to achieve this goal by performing a multilevel discourse analysis according to (1) the type of feature studied (shallow, syntactic, semantic, and discourse-related); (2) the texts at a document level; and (3) the textual genres of news, reviews, and children’s tales. To accomplish this, several corpora from the three textual genres were gathered from different sources to ensure a heterogeneous representation, paying attention to the presence and frequency of a series of features extracted with computational tools. This deep analysis aims at obtaining more detailed knowledge of the different linguistic phenomena that directly shape each of the genres included in the study, therefore showing the particularities that make them be considered as individual genres but also comprise them inside the narrative typology. The findings suggest that this type of multilevel linguistic analysis could be of great help for areas of research within natural language processing such as computational narratology, as they allow a better understanding of the fundamental features that define each genre and its communicative purpose. Likewise, this approach could also boost the creation of more consistent automatic story generation tools in areas of language generation. Full article
(This article belongs to the Special Issue Information Extraction and Language Discourse Processing)
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