The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their sentiments analysed and evolved over time, is getting much attention with increasing feedback public customers on social media. immense advancements this field urged the researchers to devise new techniques approaches, each sermonizing a different research analysis/question, that cope upcoming issues complex scenarios Analysis. Therefore, survey emphasized challenges related extraction relevant sentiments,...

Due to the high maneuverability and strong adaptability, autonomous unmanned aerial vehicles (UAVs) are of interest many civilian military organizations around world. Automatic path planning which autonomously finds a good enough that covers whole area interest, is an essential aspect UAV autonomy. In this study, we focus on automatic heterogeneous UAVs with different flight scan capabilities, try present efficient algorithm produce appropriate paths for UAVs. First, models built, abstracted...

Abstract Surface ablation temperature and linear rate are two crucial indicators for ceramic coatings under ultrahigh temperatures service, yet the results collection of such in process is difficult due to long‐period material preparation high‐cost test. In this work, four kinds machine learning models applied predict above indicators. The Random Forest (RF) model exhibits a high accuracy 87% predicting surface temperature, while low 60% rate. To optimize model, novel features constructed...

The existing models for multi-modal fake news detection focus mainly on capturing common similar semantics between different modalities to improve performance. However, they ignore the extraction of inconsistent features these modalities. intuitive cognition way people identify a piece is generally discover if there are among content itself and its comments, which could be abstracted as "comparing image-text consistency - finding valuable comments reasoning in-/consistency comments"....

With the rise of social media, spread fake news has become a significant concern, potentially misleading public perceptions and impacting stability. Although deep learning methods like CNNs, RNNs, Transformer-based models BERT have enhanced detection. However, they primarily focus on content do not consider context during propagation. Graph-based techniques incorporated but are limited by need for large labeled datasets. To address these challenges, this paper introduces GAMC, an...

Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of is nontransparent and unexplained. Simultaneously, discovered aimed at interpretability whole sequence claims but insufficient to focus on false parts claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) verification. Specifically, first...

Lianwei Wu, Yuan Rao, Haolin Jin, Ambreen Nazir, Ling Sun. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint (EMNLP-IJCNLP). 2019.

The existing data-driven approaches typically capture credibility-indicative representations from relevant articles for fake news detection, such as skeptical and conflicting opinions. However, these methods still have several drawbacks: 1) Due to the difficulty of collecting news, capacity datasets is relatively small; 2) there considerable unverified that lacks voices in articles, which makes it difficult identify their credibility. Especially, differences between true are not limited...

The existing approaches based on different neural networks automatically capture and fuse the multimodal semantics of news, which have achieved great success for fake news detection. However, they still suffer from limitations both shallow fusion features less attention to inconsistency between modalities. To overcome them, we propose multi-reading habits reasoning (MRHFR) multi-modal In MRHFR, inspired by people's reading summarize three basic cognitive put forward cognition-aware layer...

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture top-k check-worthy or salient words as evidence, but this difficult focus on questionable parts unverified claims. Besides, they utilize indiscriminately, ignoring source credibility these articles, which may cause quiet few unreliable interfere with assessment results. In paper, we propose Evidence-aware Hierarchical Interactive...

Existing approaches construct appropriate interaction models to explore semantic conflicts between claims and relevant articles, which provides practical solutions for interpretable claim verification. However, these are not necessarily all about questioning the false part of claims, makes considerable difficult be used as evidence explain results In this paper, we propose inference networks (EVIN), focus on core semantics serve Specifically, EVIN first captures segments users' principal...

Large Language Models (LLMs) have permeated various Natural Processing (NLP) tasks. For the summarization tasks, LLMs can generate well-structured rationales, which consist of Essential Aspects (EA), Associated Sentences (AS) and Triple Entity Relations (TER). These rationales guide smaller models (≤1B) to produce better summaries. However, their high deployment costs (≥70B), such as substantial storage space computing requirements, limit utilization in resource-constrained environments....

Fact verification task has emerged as an essential research topic recently due to abundant fake news spreading on the Internet. The based unstructured data (i.e., news) achieved great development, but structured table) is still in primary development period. existing methods usually construct complete heterogeneous graph networks around statement, table, and program subgraphs, then infer learn similar semantics them for fact verification. However, they generally connect nodes with same...

One of the interesting trending phenomena in sentiment analysis is prediction given by user towards an aspect term. Till today, a considerable number researchers have proposed varying methodologies for predicting aspect-based sentiments. But they mostly encapsulate semantic information manifesting themselves within local boundary around each term and overlook capturing concept that conveyed entire review (global). Therefore, this study proposes model, <italic...

How to classify incredible messages has attracted great attention from academic and industry nowadays. The recent work mainly focuses on one type of (a.k.a rumors or fake news) achieves some success detect them. existing problem is that have different types social media, news cannot represent all messages. Based this, in the paper, we divide media into five based three dimensions information evaluation metrics. And a novel method proposed deep learning for classifying media. More...

The majority of existing methods for fake news detection universally focus on learning and fusing various features detection. However, the is independent, which leads to a lack cross-interaction fusion between social media, especially posts comments. Generally, in news, there are emotional associations semantic conflicts How represent fuse both key challenge. In this paper, we propose Adaptive Interaction Fusion Networks (AIFN) fulfill among AIFN, discover conflicts, design gated adaptive...

Lianwei Wu, Yuan Rao, Yuqian Lan, Ling Sun, Zhaoyin Qi. Proceedings of the 59th Annual Meeting Association for Computational Linguistics and 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.