Between the birth of the Internet and 2003, year of birth of social networks such as MySpace, Delicious, LinkedIn, and Facebook, there were just a few dozen exabytes of information on the Web. Today, that same amount of information is created weekly. The advent of the Social Web has provided people with new content-sharing services that allow them to create and share their own contents, ideas, and opinions, in a time- and cost-efficient way, with virtually millions of other people connected to the World Wide Web. This huge amount of information, however, is mainly unstructured (because it is specifically produced for human consumption) and hence not directly machine-processable.
The automatic analysis of text involves a deep understanding of natural language by machines, a reality from which we are still very far off.
Hitherto, online information retrieval, aggregation, and processing have mainly been based on algorithms relying on the textual representation of webpages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting the number of words. When it comes to interpreting sentences and extracting meaningful information, however, their capabilities are known to be very limited, as most of the existing approaches are still based on the syntactic representation of text, a method that relies mainly on word co-occurrence frequencies.
Such algorithms are limited by the fact that they can process only the information that they can `see\'.
As human text processors, we do not have such limitations as every word we see activates a cascade of semantically related concepts, relevant episodes, and sensory experiences, all of which enable the completion of complex NLP tasks -- such as word-sense disambiguation, textual entailment, and semantic role labeling -- in a quick and effortless way.
Knowledge-based NLP focuses on the intrinsic meaning associated with natural language text. Rather than simply processing documents at syntax-level, knowledge-based approaches rely on implicit denotative features associated with natural language text, hence stepping away from the blind usage of word co-occurrence count.
Unlike purely syntactical techniques, knowledge-based approaches are also able to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.
TOPICS OF INTEREST
Articles are invited in area of knowledge-based systems for natural language processing and understanding. The broader context of the Special Issue comprehends artificial intelligence, knowledge representation and reasoning, data mining, transfer learning, knowledge acquisition, neural networks, semantic networks, web ontologies, and more. Topics include, but are not limited to:
Document retrieval and classification
Information retrieval and extraction
Topic modeling, topic spotting, and topic segmentation
Aspect extraction and named-entity recognition
Textual entailment and semantic role labeling
Sentiment analysis and subjectivity detection
Text summarization and question answering
Machine translation and microtext analysis
Word-sense disambiguation and anaphora resolution
Time-evolving topic and sentiment tracking
Multimodal fusion for continuous interpretation of semantics
Semantic multi-dimensional scaling and common-sense reasoning
Sarcasm detection and intention mining
Semi-supervised learning and domain adaptation
Human-agent, -computer, and -robot interaction
The Special Issue also welcomes papers on specific application domains of knowledge-based natural language processing, e.g., user profiling and personalization, customer experience management, intelligent user interfaces, multimedia management, computer-mediated human-human communication, enterprise feedback management, social media marketing, and cyber-issue detection. The authors will be required to follow the Author\'s Guide for manuscript submission to Knowledge-Based Systems. Authors shall choose “SI: KBNLP” under “article type name” when submitting on ees.elsevier.com/knosys
Call for papers out: June 20th, 2015
Paper submission : October 30th, 2015
First revision: January 10th, 2016
Updated versions: March 15th, 2016
Second revision: April 30th, 2016
Final version: June 15th, 2016
Erik Cambria, Nanyang Technological University, Singapore
Bjoern Schuller, Imperial College London, UK
Yunqing Xia, Tsinghua University, China
Bebo White, Stanford University, USA