Results 51 to 60 of about 2,548,691 (245)
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
Advanced Robotics Research, EarlyView.BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat +4 more
wiley +1 more source
Word sense disambiguation for spam filtering
Electronic Commerce Research and Applications, 2012 Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms, and phishing. More than 86% of received e-mails are spam. Historical approaches to combating these messages, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable ...
Carlos Laorden +4 more
openaire +2 more sources
Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models
Advanced Robotics Research, EarlyView.A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki +2 more
wiley +1 more source
Durham - a word sense disambiguation system [PDF]
, 1999 Ever since the 1950's when Machine Translation first began to be developed, word sense disambiguation (WSD) has been considered a problem to developers. In more recent times, all NLP tasks which are sensitive to lexical semantics potentially benefit from
Hawkins, Paul Martin
core
Journal of the Association for Information Science and Technology, EarlyView.Abstract Context‐centric proactive information delivery (PID) is a relatively underexplored domain within recommender systems (RS) aimed at enhancing Knowledge Workers' productivity by proactively providing relevant information during digital tasks.
Mahta Bakhshizadeh +4 more
wiley +1 more source
Chinese Word Sense Disambiguation using a LSTM
ITM Web of Conferences, 2017 Word sense disambiguation (WSD) is a challenging natural language processing (NLP) problem. We propose a new strategy for WSD, which at first replaces the interesting word in a sentence by the different synonyms corresponding to the different meanings ...
Sun Xue-Ren +3 more
doaj +1 more source
Sometimes less is more : Romanian word sense disambiguation revisited [PDF]
, 2007 Recent approaches to Word Sense Disambiguation (WSD) generally fall into two classes: (1) information-intensive approaches and (2) information-poor approaches.
Kübler, Sandra, Dinu, Georgiana
core
Wiki sense bag creation using multilingual word sense disambiguation [PDF]
, 2022 Performance of word sense disambiguation (WSD) is one of the challenging tasks in the area of natural language processing (NLP). Generation of sense annotated corpus for multilingual word sense disambiguation is out of reach for most languages even if ...
Shreya Patankar +5 more
core +1 more source
Clinical Anatomy, EarlyView.ABSTRACT Surgical safety depends on recognition of patient‐specific anatomy when encountered structures depart from population norms. Knowledge of anatomical variations is often treated as supplemental rather than as a safety‐relevant necessity.
Juan A. Sanchis‐Gimeno +4 more
wiley +1 more source
TWE‐WSD: An effective topical word embedding based word sense disambiguation
CAAI Transactions on Intelligence Technology, 2021 Word embedding has been widely used in word sense disambiguation (WSD) and many other tasks in recent years for it can well represent the semantics of words.
Lianyin Jia +5 more
doaj +1 more source