LMNLP 2023

A language model (LM) is the basic part of Natural Language Processing (NLP). It’s a statistical and probabilistic technique that analyzes the pattern of human language and determines the probability of a given sequence of words occurring in a sentence to provide a basis for their word predictions. It is used for a variety of NLP tasks such as audio to text conversion, speech recognition, sentiment analysis, Big Data or small data sets analysis, summarization, machine translation and question answering, etc.

Recently a Large, pre-trained transformer-based Neural language models have changed the Natural Language Processing (NLP) field to solve tasks via pre-training then fine-tuning, prompting, text generation or generate data for training augmentation approaches, etc..

This special session aims to identify and explore the current challenges in Language Models Development. The goal is to to provide an overview of the new research being carried out in the NLP area focusing on emerging Language models, machine learning, deep learning methods and approaches for single and multiple language learning, understanding, generation and grounding, question answering and information retrieval, as well as applications of them to different domains like e-health, e-learning and e-legal.

Finally, this special session aims to gather researchers,developers, and all those working in these fields of research to share their ideas and to discuss their work as well as perspectives on future directions in this exciting field. Original contributions are sought, covering the whole range of theoretical and practical aspects in this research area.

LMNLP2023 has the goal of a broad technical program. Relevant topics for the Session include, but are not limited to, the following areas (in alphabetical order):

Topics:

• Big Data Management
• Big Text Mining and Information Extraction
• Big Data Analytics and Information Retrieval
• Artificial Intelligence
• Chatbots
• Computational Social Science and Cultural Analytic
• Data analytics and mining for decision support
• Deep-Learning, and Machine Learning for NLP
• Dialogue and Interactive System
• Efficient Methods for NLP
• Interpretability and Analysis of Models for NLP
• Linguistic Theories, Cognitive Modeling and Psycholinguistics
• Machine Translation and Multilingualism
• Phonology, Morphology and Word Segmentation
• Question Answering
• Resources and Evaluation
• Semantics: Lexical, Sentence level, Textual Inference and Other areas
• Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Summarization
• Cognitive semantics,
• Logics of language,andLanguage modeling,
• Computational linguistics (lexicology; morphology; syntax; semantics),
• Domain ontologies, linguistic ontologies,
• Knowledge processing,
• Topic Detection