Results 121 to 130 of about 21,648 (265)
Classifier Performance on Long‐Tail Distributions
Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 18, Issue 4, August 2025.ABSTRACT This paper introduces a Gaussian mixture model designed to explore the implications of long‐tailedness on classification performance. Our study reveals that simple under‐specified classifiers are inherently limited in reducing generalization error within this framework, a limitation overcome by well‐specified and even more complex over ...
Artur Pak +5 more
wiley +1 more source
Modern Approaches to Aspect-Based Sentiment Analysis
Труды Института системного программирования РАН, 2018 The paper presents a survey of methods solving the actual task of aspect-based sentiment analysis. Solutions for this task were proposed at multiple natural language processing conferences.
I. . Andrianov +2 more
doaj +1 more source
MIND at SemEval-2023 Task 11: From Uncertain Predictions to Subjective Disagreement [PDF]
, 2023 Giulia Rizzi +4 more
openalex +1 more source
International Workshop on Semantic Evaluation, 2023 One of the most extensively researched applications in natural language processing (NLP) is sentiment analysis. While the majority of the study focuses on high-resource languages (e.g., English), this research will focus on low-resource African languages
Nilanjana Raychawdhary +3 more
semanticscholar +1 more source
Joint Extraction Method for Spatial Relations in Chinese Geological Texts
Earth and Space Science, Volume 12, Issue 8, August 2025.Abstract Extracting spatial relations of geological entities is an important prerequisite for achieving natural language processing tasks such as geological knowledge question answering and semantic search, and is an important means to achieve structural reconstruction of unstructured geological data.
Chuan Chen +9 more
wiley +1 more source
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceIn recent years, large-scale language models (LLMs) have nearly become the dominant force in almost every natural language processing (NLP) task. The primary research approach has focused on selecting the most appropriate language model for specific NLP ...
Linrui Zhang +2 more
doaj +1 more source
AutoSense Model for Word Sense Induction
, 2018 Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are
Amplayo, Reinald Kim +2 more
core +1 more source
Expert Systems, Volume 42, Issue 7, July 2025.ABSTRACT Instant messenger Telegram has emerged as a favoured platform for far‐right activism, conspiracy theories, political propaganda, and misinformation, which has its own target audience. This study explores the application of multilingual pre‐trained language models to detect and measure toxicity in political content on Telegram channels.
Arsenii Tretiakov +3 more
wiley +1 more source
ShotgunWSD 2.0: An Improved Algorithm for Global Word Sense Disambiguation
IEEE Access, 2019 ShotgunWSD is a recent unsupervised and knowledge-based algorithm for global word sense disambiguation (WSD). The algorithm is inspired by the Shotgun sequencing technique, which is a broadly-used whole genome sequencing approach. ShotgunWSD performs WSD
Andrei M. Butnaru, Radu Tudor Ionescu
doaj +1 more source
SemEval-2016 Task 6: Detecting Stance in Tweets
International Workshop on Semantic Evaluation, 2016 Here for the first time we present a shared task on detecting stance from tweets: given a tweet and a target entity (person, organization, etc.), automatic natural language systems must determine whether the tweeter is in favor of the given target ...
Saif M. Mohammad +4 more
semanticscholar +1 more source