In this paper, we introduce EVALution 1.0, a dataset designed for the training and evaluation of Distributional Semantic Models (DSMs).This version consists almost 7.5K tuples, instantiating several semantic relations between word pairs (including hypernymy, synonymy, antonymy, meronymy).The is enriched with large amount additional information (i.e.relation domain, frequency, POS, field, etc.) that can be used either filtering or performing an in-depth analysis results.The tuples were...

In text categorization, feature selection (FS) is a strategy that aims at making classifiers more efficient and accurate. However, when dealing with new task, it still difficult to quickly select suitable one from various FS methods provided by many previous studies. this paper, we propose theoretic framework of based on two basic measurements: frequency measurement ratio measurement. Then six popular are in detail discussed under framework. Moreover, the guidance our theoretical analysis,...

Most theories of emotion treat recognition a triggering cause event as an integral part processing. This paper proposes detection new research area in As first step toward fully automatic inference emotion‐cause correlation, we propose text‐driven, rule‐based approach to Chinese. First, constructed Chinese annotated corpus based on our proposed annotation scheme. Next, analyzed the data, which yielded identification seven groups linguistic cues and two sets generalized rules for causes. We...

Attention models are proposed in sentiment analysis because some words more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction is first built using data as dependent and other features the independent The predicted time then used to build (CBA) layer for neural analysis. As comprehensive model,...

Modality exclusivity norms have been developed in different languages for research on the relationship between perceptual and conceptual systems. This paper sets up first modality Chinese, a Sino-Tibetan language with semantics as its orthographically relevant level. The are collected through two studies based Chinese sensory words. experimental designs take into consideration morpho-lexical orthographic structures of Chinese. Study 1 provides set Mandarin single-morpheme words mean ratings...

The rise of misinformation online and offline reveals the erosion long-standing institutional bulwarks against its propagation in digitized era.Concerns over problem are global impact is long-lasting.The past few decades have witnessed critical role detection enhancing public trust social stability.However, it remains a challenging for Natural Language Processing community.This paper discusses main issues with comprehensive review on representative works terms methods, feature...

In four separate essays the authors address complex and difficult connections among grammatical theory, mathematical linguistics, operation of real natural-language-processing systems, both human electronic. The editors' introduction details progress problems involved in attempts to relate these areas research. William Rounds discusses relevance complexity results linguistics computational providing useful caveats about how might be misinterpreted pointing out promising avenues future...

Attention models are proposed in sentiment analysis and other classification tasks because some words more important than others to train the attention models. However, most existing methods either use local context based information, affective lexicons, or user preference information. In this work, we propose a novel model trained by cognition grounded eye-tracking data. First,a reading prediction is built using data as dependent features independent The predicted time then used build layer...

Large language models (LLMs) demonstrate strong capabilities in natural processing but remain prone to hallucinations, generating factually incorrect or fabricated content. This issue undermines their reliability, particularly high-stakes domains such as healthcare and legal advisory. To address this challenge, we propose Delta, an inference-time method that reduces hallucinations without requiring model retraining additional data. Delta works by randomly masking parts of the input prompt...