Классификация отношений

A major focus of current work in distri- butional models of semantics is to con- struct phrase representations composition- ally from word representations. However, the syntactic contexts which are modelled …...

Active learning (AL) has been proven ef- fective to reduce human annotation ef- forts in NLP. However, previous studies on AL are limited to applications in a single language. This …...

The state-of-the-art methods used for relation classification are primarily based on statistical ma- chine learning, and their performance strongly depends on the quality of the extracted features. The extracted features …...

We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific fea- tures on …...

AbstractThis paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) …...

AbstractNowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model …...

Relation classification is an important se- mantic processing task in the field of natu- ral language processing (NLP). In this pa- per, we present a novel model BRCNN to classify …...

We address relation classification in the con- text of slot filling, the task of finding and eval- uating fillers like “Steve Jobs” for the slot X in “X founded Apple”. …...

AbstractNowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with …...

This paper connects a vector-based com- position model to a formal semantics, the Dependency-based Compositional Se- mantics (DCS). We show theoretical evi- dence that the vector compositions in our model …...

AbstractRelation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially …...

AbstractThis paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces …...

We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness. We cast the problem as relation classifica- …...

AbstractThe task of relation classification in the biomedical domain is complex due to the presence of samples obtained from heterogeneous sources such as research articles, discharge summaries, or electronic health …...

AbstractThis paper describes our TTI-COIN system that participated in SemEval-2017 Task 10. We investigated appropriate embeddings to adapt a neural end-to-end entity and relation extraction system LSTM-ER to this task. …...

AbstractRelation Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most …...

Relation classification is the task to predict semantic relations between pairs of entities in a given text. In this paper, a novel Long Short Term Memory Network (LSTM)-based approach is …...

This paper proposes a novel attention mecha- nism, customized attention mechanism, for rela- tion classification. Based on the task require- ments, logical constraints of the attention mech- anism have been …...

AbstractReliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language …...

AbstractThe state-of-the-art methods for relation classification are primarily based on deep neural net- works. This kind of supervised learning method suffers from not only limited training data, but also the …...

AbstractRationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation …...

AbstractRelation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the …...

AbstractThis paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different …...

AbstractThis article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest dependency path (sdp) between …...

AbstractDistant supervision has obtained great progress on relation classification task. However, it still suffers from noisy labeling problem. Different from previous works that underutilize noisy data which inherently characterize the …...

AbstractRecently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based …...

AbstractRelation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of …...

AbstractWe study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challenging because rule-induced weak …...

AbstractThis paper describes our approach to “DeftEval: Extracting Definitions from Free Text in Textbooks” competition held as a part of Semeval 2020. The task was devoted to finding and labeling …...

AbstractRelation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen …...

AbstractThe idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification …...

AbstractRelation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering. Current approaches for …...

AbstractThis work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different …...

AbstractRelation classification (sometimes called ‘extraction’) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challenging for Indian languages, because they are syntactically and …...

AbstractResearch on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose …...

AbstractRelation extraction is a subtask of natural langage processing that has seen many improvements in recent years, with the advent of complex pre-trained architectures. Many of these state-of-the-art approaches are …...

AbstractPre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among …...

We study the problem of injecting knowl- edge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, …...

AbstractDistant supervision can generate large-scale relation classification data quickly and economi-cally. However a great number of noise sentences are introduced which can not express their labeled relations. By means of …...

AbstractWe explore few-shot learning (FSL) for relation classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories …...

AbstractWe study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training …...

AbstractIn recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, …...

AbstractMany joint entity relation extraction models setup two separated label spaces for the two sub-tasks (i.e., entity detection and relation classification). We argue that this setting may hinder the information …...

AbstractCorrect natural language understanding requires computers to distinguish the literal and metaphorical senses of a word. Recent neu- ral models achieve progress on verb metaphor detection by viewing it as …...

AbstractWe cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a …...

AbstractPre-trained language models (PLMs) such as BERT and RoBERTa have dramatically improved the performance of various natural language processing tasks. Although these models are trained on large amounts of raw …...

AbstractThis paper presents our solution for SemEval-2022 Task 10: Structured Sentiment Analysis. The solution consisted of two modules: the first for sequence tagging and the second for relation classification. In …...

AbstractRelational triple extraction is a critical task for natural language processing. Existing methods mainly focused on capturing semantic information, but suffered from ignoring the syntactic structure of the sentence, which …...

AbstractThere has been a surge of interest in utilizing Knowledge Graphs (KGs) for various natural language processing/understanding tasks. The conventional mechanism to retrieve facts in KGs usually involves three steps: …...