The current popular approach to the extraction of document-level relations is mainly based on either a graph structure or serialization model method for the inference, but the graph structure method makes the model complicated, while the serializatio...
Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that support important evidences for sent...
Previous work has demonstrated that end-to-end neural sequence models work well for document-level event role filler extraction. However, the end-to-end neural network model suffers from the problem of not being able to utilize global information, re...
Document-level event argument extraction (DEAE) aims to identify the arguments corresponding to the roles of a given event type in a document. However, arguments scattering and arguments and roles overlapping make DEAE face great challenges. In this...
Document-level event extraction (DEE) aims at extracting event records from given documents. Existing DEE methods handle troublesome challenges by using multiple encoders and casting the task into a multi-step paradigm. However, most of the previous...
Structured extraction of emergency event information can effectively enhance the ability to respond to emergency events. This article focuses on extracting Chinese document-level emergency events, which entails addressing two key issues in this field...
Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data...
Event argument extraction is a crucial subtask of event extraction, which aims at extracting arguments that correspond to argument roles when given event types. The majority of current document-level event argument extraction works focus on extractin...
Question answering from visually rich documents (VRDs) is the task of retrieving the correct answer to a natural language question by considering the content of textual and visual elements in the document, as well as the pages’ layout. To answe...
Relation extraction in the threat intelligence domain plays an important role in mining the internal association between crucial threat elements and constructing a knowledge graph (KG). This study designed a novel document-level relation extraction m...
Event–event causal relation extraction (ECRE) represents a critical yet challenging task in natural language processing. Existing studies primarily focus on extracting causal sentences and events, despite the use of joint extraction methods for...
This article helps establish reliable baselines for document-level sentiment analysis in highly inflected languages like Czech and Slovak. We revisit an earlier study representing the first comprehensive formulation of such baselines in Czech and sho...
Considerable research has been conducted to obtain translations that reflect contextual information in documents and simultaneous interpretations. Most of the existing studies use concatenation data which merge previous and current sentences for trai...
Document-level links between repeated mentions can provide contextual evidence unavailable to a local entity-type classifier, a potentially important capability for Ukrainian because of its limited annotated resources and rich inflectional morphology...
Document-level Event Argument Extraction (DEAE) aims to extract structural event knowledge composed of arguments and roles beyond the sentence level. Existing methods mainly focus on designing prompts and using Abstract Meaning Representation (AMR) g...
Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing press article...
Current state-of-the-art neural machine translation (NMT) architectures usually do not take document-level context into account. However, the document-level context of a source sentence to be translated could encode valuable information to guide the...
Aspect category sentiment analysis aims to automatically identify the sentiment polarities of aspect categories mentioned in text, and is widely used in the data analysis of product reviews and social media. Most existing studies typically limit them...
Claim extraction and decontextualization are fundamental steps in automated fact-checking tasks. Claim extraction aims to identify core check-worthy claims from unverified text, while decontextualization seeks to eliminate the contextual dependencies...
Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while m...
Automated sentiment analysis of online-education reviews is useful for understanding learner feedback. Classification-based methods usually capture only document-level polarity. They may miss aspect-level signals and may collapse to the majority clas...
Predicting future events is crucial for temporal reasoning, providing valuable insights for decision-making across diverse domains. However, the intricate global interactions and temporal–causal relationships at the document level event present...
In this paper, a novel re-engineering mechanism for the generation of word embeddings is proposed for document-level sentiment analysis. Current approaches to sentiment analysis often integrate feature engineering with classification, without optimiz...
Tracert-retrieval-augmented generation (RAG) is a novel retrieval-augmented framework designed for efficient, document-level multi-hop reasoning. Unlike conventional RAG systems that retrieve top-k text segments based solely on dense similarity, Trac...
Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entit...
An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. In this work, we propose a novel Multimodal Interactive ...
Document-level Event Extraction (DEE) requires identifying complex event records and arguments dispersed across unstructured texts. However, applying general Large Language Models (LLMs) to DEE is intrinsically hindered by their lack of inductive bia...
The ability to learn robust, resizable feature representations from unlabeled data has potential applications in a wide variety of machine learning tasks. One way to create such representations is to train deep generative models that can learn to cap...
Given the emergence of new applications with stringent networking requirements, traditional bit-level communication may reach theoretical Shannon capacity and struggle to support such applications. Therefore, the idea of semantic communication (SC) h...
Relation extraction (RE) is the core link of downstream tasks, such as information retrieval, question answering systems, and knowledge graphs. Most of the current mainstream RE technologies focus on the sentence-level corpus, which has great limitat...
This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytic policy-making procedures. The automation of legal document extraction...
Inclusive education is an important foundation of many societies, including the post-Soviet countries. It has been more than ten years since the official implementation of inclusion in Russia. However, the inclusive education system has not developed...
This paper introduces a multi-layered cross-genre corpus, annotated for coreference resolution, causal relations, and temporal relations, comprising a variety of genres, from news articles and children’s stories to Reddit posts. Our results rev...
Event extraction aims to identify and structure event information from unstructured text, playing a critical role in real-world applications such as news analysis, public opinion discovery, and intelligence gathering. Traditional approaches, however,...
This study examines whether administrative simplification is associated with stronger document-management practices in a district-level local government organization, and why this matters for societal outcomes such as transparency and more equitable...
Although JSON documents are being used in several emerging applications (e.g., Big Data applications, IoT, mobile computing, smart cities, and online social networks), there is no consensual or standard language for updating JSON documents (i.e., cre...
Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations betwe...
Inconsistent and erroneous file naming is a recurring information management problem in BIM and Common Data Environment (CDE) workflows, affecting document identification, retrieval, traceability, and control. This problem was observed during work on...
AI-based text classification is a process to classify Arabic contents into their categories. With the increasing number of Arabic texts in our social life, traditional machine learning approaches are facing different challenges due to the complexity...
Power system testing and inspection documents are multimodal and highly structured, making content-level audit labeling challenging due to scattered evidence and cross-component dependencies. We propose EviCal, an evidence-grounded consistency calibr...
This paper shows an interesting case of coastal landscape reconstruction by using innovative marine robotic instrumentation, applied to an archaeological key-site in the Campi Flegrei (Italy), one of the more inhabited areas in the Mediterranean duri...
Sentiment analysis is widely used to quantify the emotional tone of text. However, many pretrained sentiment models are only designed for short inputs and can struggle when applied to longer compilations of text or even full documents. These limitati...
Neural auto-regressive sequence-to-sequence models have been dominant in text generation tasks, especially the question generation task. However, neural generation models suffer from the global and local semantic semantic drift problems. Hence, we pr...
Multi-label text classification (MLTC) aims to assign the most appropriate label or labels to each input text. Previous studies have focused on mining textual information, ignoring the interdependence of labels and texts, thus leading to the loss of...
The role of technology in translator training has become increasingly significant as machine translation (MT) evolves at a rapid pace. Beyond practical tool usage, training must now prepare students to engage critically with MT outputs and understand...
With the increasing air traffic flow, the workload of air traffic controllers is also growing, and their proficiency directly impacts civil aviation safety and efficiency. To address the lack of clear training objectives and inconsistent evaluation m...
Software clones are code fragments with similar or nearly similar functionality or structures. These clones are introduced in a project either accidentally or deliberately during software development or maintenance process. The presence of clones pos...
Customer reviews on the Internet reflect users’ sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information...
The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event argument...
Nowadays, a large amount of information is stored as text, and numerous text mining techniques have been developed for various applications, such as event detection, news topic classification, public opinion detection, and sentiment analysis. Althoug...
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