Results 171 to 180 of about 200,375 (331)
Benchmarking Large Language Models for Polymer Property Predictions
Large language models (LLMs) are fine‐tuned on polymer thermal property datasets to directly predict glass transition, melting, and decomposition temperatures from SMILES inputs. Compared to state‐of‐the‐art models such as Polymer Genome, polyGNN, and polyBERT, LLMs achieve competitive yet lower accuracy.
Sonakshi Gupta +3 more
wiley +1 more source
Computational Linguistics and Deep Learning
Christopher D. Manning
doaj +1 more source
Evidence in clinical reasoning: a computational linguistics analysis of 789,712 medical case summaries 1983-2012. [PDF]
Seidel BM, Campbell S, Bell E.
europepmc +1 more source
Measuring Corporate Weather Exposure using Computational Linguistics
Venky Nagar, Jordan Schoenfeld
openalex +1 more source
Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery
ElementBERT extracts semantic embeddings of chemical elements from 1.29 million alloy‐related abstracts, providing robust descriptors that improve prediction accuracy by up to 23% across titanium, high‐entropy, and shape memory alloys, with demonstrated generalization on alloy compositions reported in 2025.
Yunze Jia +7 more
wiley +1 more source
Computational Linguistics: Models, Resources, Applications
Anna Feldman
doaj +1 more source
Pasteur's quadrant, computational linguistics, LSA, education [PDF]
Thomas K. Landauer
openalex +1 more source
DeepSeek‐Lattice‐KG integrates a domain‐adapted 14B LLM with a Neo4j lattice knowledge graph distilled from 50,000 papers. It analyzes queries, retrieves supporting subgraphs, and generates grounded answers; on a 2100‐question, six‐domain benchmark, it achieves 94.8% accuracy.
Zhiyang Shu +6 more
wiley +1 more source
Workshop on Extracting and Using Constructions in Computational Linguistics [PDF]
Knutsson, Ola, Sahlgren, Magnus
core +1 more source

