Is Cantonese lexical tone information important for sentence recognition accuracy in quiet and in noise? by Yuan Chen

“…This finding can be explained using the Ease of Language Understanding model and suggests that those with higher WM are less likely to be affected by the degraded lexical information for perceiving daily-use sentences in the TTB.…”
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Large language models for human–robot interaction: A review by Ceng Zhang, Junxin Chen, Jiatong Li, Yanhong Peng, Zebing Mao

“…The fusion of large language models and robotic systems has introduced a transformative paradigm in human–robot interaction, offering unparalleled capabilities in natural language understanding and task execution. This review paper offers a comprehensive analysis of this nascent but rapidly evolving domain, spotlighting the recent advances of Large Language Models (LLMs) in enhancing their structures and performances, particularly in terms of multimodal input handling, high-level reasoning, and plan generation. …”
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Does word knowledge account for the effect of world knowledge on pronoun interpretation? by Cameron R. Jones, Benjamin Bergen

“…We address this question by focusing on a core aspect of language understanding: pronoun resolution. While existing studies suggest that comprehenders use world knowledge to resolve pronouns, the distributional hypothesis and its operationalization in large language models (LLMs) provide an alternative account of how purely linguistic information could drive apparent world knowledge effects. …”
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Sentence processing: linking language to motor chains by Fabian Chersi, Serge Thill, Tom Ziemke, Anna M Borghi, Anna M Borghi

“…According to this embodied perspective language understanding is based on a mental simulation process involving a sensory-motor matching system known as the mirror neuron system. …”
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Next-generation consumer innovation search: Identifying early-stage need-solution pairs on the web by von Hippel, Eric, Kaulartz, Sandro

“…Today, technical advances in machine learning techniques for natural language understanding, such as semantic word space models and semantic network analytics, have made it practical to capture descriptions of early-stage, need-solution pairs mentioned anywhere in the open, textual content of the Internet. …”
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A brief survey on recent advances in coreference resolution by Liu, Ruicheng, Mao, Rui, Luu, Anh Tuan, Cambria, Erik

“…Predicting coreference connections and identifying mentions/triggers are the major challenges in coreference resolution, because these implicit relationships are particularly difficult in natural language understanding in downstream tasks. Coreference resolution techniques have experienced considerable advances in recent years, encouraging us to review this task in the following aspects: current employed evaluation metrics, datasets, and methods. …”
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Model-driven smart contract generation leveraging pretrained large language models by Jiang, Qinbo

“…It compares the effectiveness of GPT-4 and LLaMA-2 in creating smart contracts, aiming to identify strengths and limitations in language understanding and code generation. By integrating LLMs with smart contract languages, the project seeks to democratise smart contract development, offering a novel tool for users with limited programming experience. …”
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Composition distillation for semantic sentence embeddings by Vaanavan, Sezhiyan

“…The increasing demand for Natural Language Processing (NLP) solutions is driven by an exponential growth in digital content, communication platforms, and the undeniable need for sophisticated language understanding. This surge in demand also reflects the critical role of NLP in enabling machines to comprehend, interpret, and generate human-like text, which makes it a crucial technology in modern AI applications. …”
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Improving language model predictions via prompts enriched with knowledge graphs by Brate, R, Dang, M-H, Hoppe, F, He, Y, Meroño-Peñuela, A, Sadashivaiah, V

“…Despite advances in deep learning and knowledge graphs (KGs), using language models for natural language understanding and question answering remains a challenging task. …”

Does Context Matter? Effective Deep Learning Approaches to Curb Fake News Dissemination on Social Media by Jawaher Alghamdi, Yuqing Lin, Suhuai Luo

“…The proposed architectures introduced a novel approach to learning rich, semantical, and contextual representations of a given news text using natural language understanding of transfer learning coupled with context-based features. …”
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A commentary of GPT-3 in MIT Technology Review 2021 by Min Zhang, Juntao Li

“…Through the development of large-scale natural language models with writing and dialogue capabilities, artificial intelligence (AI) has taken a significant stride towards better natural language understanding (NLU) and human-computer interaction (HCI). …”
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Controllable Text Generation Using Semantic Control Grammar by Hyein Seo, Sangkeun Jung, Jeesu Jung, Taewook Hwang, Hyuk Namgoong, Yoon-Hyung Roh

“…Extensive experiments and analyses are conducted on benchmark, natural language understanding, data-to-text generation, and text classification datasets. …”
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A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods by Jianjun Ni, Yinan Chen, Yan Chen, Jinxiu Zhu, Deena Ali, Weidong Cao

“…It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. …”
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Analyzing the Discourse Functions of Abbas Maroufi’s The Year of Turmoil: A Faircloughian Approach by Tannaz Teymouri, Sayyed Ahmad Parsa, Yadgar Karimi

“…In novels, fictional life is intertwined with social realities, power dynamics, and ideologies, which authors represent through language. Understanding the intellectual and ideological tendencies of these writers has led to a growing interest among researchers and literary critics, necessitating the use of text analysis methods. …”
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Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, Dimitrios K. Nasiopoulos

“…Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. …”
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Triple Channel Feature Fusion Few-Shot Intent Recognition With Orthogonality Constrained Multi-Head Attention by Di Wu, Yuying Zheng, Peng Cheng

“…Intent recognition in few-shot scenarios is a hot research topic in natural language understanding tasks. Aiming at the problems of insufficient consideration of fine-grained features of the text and insufficient training of features in the process of model fine-tuning, the Triple Channel IntentBERT and Orthogonality Constrained Multi-Head Attention Model (TMH-IntentBERT) is proposed. …”
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Improving the performance of graph based dependency parsing by guiding bi-affine layer with augmented global and local features by Mücahit Altıntaş, A. Cüneyd Tantuğ

“…The growing interaction between humans and machines raises the necessity to more sophisticated tools for natural language understanding. Dependency parsing is crucial for capturing the semantics of a sentence. …”
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CAILIE 1.0: A dataset for Challenge of AI in Law - Information Extraction V1.0 by Yu Cao, Yuanyuan Sun, Ce Xu, Chunnan Li, Jinming Du, Hongfei Lin

“…Considering that information extraction is mainly regarded as the first step in natural language understanding, the quality of legal information extraction results certainly has an immense impact on the performance of various legal artificial intelligence (AI) downstream tasks. …”
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Leveraging Dialogue State Tracking for Zero-Shot Chat-Based Social Engineering Attack Recognition by Nikolaos Tsinganos, Panagiotis Fouliras, Ioannis Mavridis

“…Novel modeling techniques are being developed to enhance natural language understanding, natural language generation, and dialogue-state tracking. …”
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An Embedding-Based Approach to Repairing OWL Ontologies by Qiu Ji, Guilin Qi, Yinkai Yang, Weizhuo Li, Siying Huang, Yang Sheng

“…High-quality ontologies are critical to ontology-based applications, such as natural language understanding and information extraction, but logical conflicts naturally occur in the lifecycle of ontology development. …”
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