Pair Extraction Research Articles

Deep Neural Networks Based on Span Association Prediction for Emotion-Cause Pair Extraction.

The emotion-cause pair extraction task is a fine-grained task in text sentiment analysis, which aims to extract all emotions and their underlying causes in a document. Recent studies have addressed the emotion-cause pair extraction task in a step-by-step manner, i.e., the two subtasks of emotion extraction and cause extraction are completed first, followed by the pairing task of emotion-cause pairs. However, this fail to deal well with the potential relationship between the two subtasks and the extraction task of emotion-cause pairs. At the same time, the grammatical information contained in the document itself is ignored. To address the above issues, we propose a deep neural network based on span association prediction for the task of emotion-cause pair extraction, exploiting general grammatical conventions to span-encode sentences. We use the span association pairing method to obtain candidate emotion-cause pairs, and establish a multi-dimensional information interaction mechanism to screen candidate emotion-cause pairs. Experimental results on a quasi-baseline corpus show that our model can accurately extract potential emotion-cause pairs and outperform existing baselines.

A Multi-Scale and Lightweight Bearing Fault Diagnosis Model with Small Samples

Currently, deep-learning-based methods have been widely used in fault diagnosis to improve the diagnosis efficiency and intelligence. However, most schemes require a great deal of labeled data and many iterations for training parameters. They suffer from low accuracy and over fitting under the few-shot scenario. In addition, a large number of parameters in the model consumes high computing resources, which is far from practical. In this paper, a multi-scale and lightweight Siamese network architecture is proposed for the fault diagnosis with few samples. The architecture proposed contains two main modules. The first part implements the feature vector extraction of sample pairs. It is composed of two lightweight convolutional networks with shared weights symmetrically. Multi-scale convolutional kernels and dimensionality reduction are used in these two symmetric networks to improve feature extraction and reduce the total number of model parameters. The second part takes charge of calculating the similarity of two feature vectors to achieve fault classification. The proposed network is validated by multiple datasets with different loads and speeds. The results show that the model has better accuracy, fewer model parameters and a scale compared to the baseline approach through our experiments. Furthermore, the model is also proven to have good generalization capability.

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain.

Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper.

Span-based relational graph transformer network for aspect–opinion pair extraction

Span-based relational graph transformer network for aspect–opinion pair extraction

Corpus-based bilingual terminology extraction in the power engineering domain

Abstract This paper presents the resources and tools used to extract and evaluate bilingual, English-Serbian terminology in the power engineering domain. The resources consist of existing general and domain lexica, and a domain parallel corpus; tools include term extractors for both languages and a tool for aligning the segments belonging to corpus sentences. The system was tested by varying a match function that establishes the presence of an extracted term in an aligned segment (a chunk), ranging from very loose to strict. The evaluation of results showed that the precision of English term extraction was 92%, Serbian term extraction 86%, while the precision of bilingual pair extraction was 72% based on the strictest match function. The result of extraction was 2,684 correct bilingual pairs that enhanced the terminology database and can further be used to support the search of the power engineering aligned collection stored in a digital library.

Pyro-phototronic effect enhanced self-powered photodetector

Pyro-phototronic effect has attracted much attention in fabricating self-powered high-performance photodetectors because of the significant enhancement by modulating the separation, transportation, and extraction of photo-generated electron-hole pairs in optoelectronic processes. This review highlights the advances in fabricating pyro-phototronic effect enhanced photodetectors. The fundamental research on the enhanced mechanism is discussed. Various device structures and figure of merit are summarized to demonstrate the improvement of performance by pyro-phototronic effect. Synergetic effects dominated by pyro-phototronic effect are also discussed to show their great potential and the universality of applying pyro-phototronic effect in photodetection. Finally, an outlook is delivered, and future research directions and challenges are discussed.

Synchronously tracking entities and relations in a syntax-aware parallel architecture for aspect-opinion pair extraction

Synchronously tracking entities and relations in a syntax-aware parallel architecture for aspect-opinion pair extraction

Fluorometric quantification of alkaloids in the homeopathic mother tinctures of Vinca minor L. and Fumaria officinalis L.

To assess the toxic potential of the alkaloids, a quantification method is necessary. An ion pair extraction method was used for quantitative fluorometric determination of vincamine, protopine and all contained alkaloids in the mother tinctures of Vinca minor and Fumaria officinalis. The non-fluorescent alkaloids were transformed into an ion pair with sodium-9,10-dimethoxy-anthracene-sulfonate and then fluorometrically determined and quantified in this study. The applicable ion pair was extracted in a suitable organic solvent, where dichloromethane has proven to be beneficial. Conditions for the ion pairing and fluorometric quantification are given. The recovery rate was used to investigate the quality of determinability and the influence of the mother tincture matrix. The method was applied to determine the concentration of protopine in the range 0.1 - 15 μg/ml and of vincamine in the range of 0.5 - 20 μg/ml. The limit of detection was < 0.3 μg/ml, and the limit of quantification < 0.9 μg/ml for both alkaloids.

КОРЕФЕРЕНТНІСТЬ У ПОЕТИЧНИХ ТВОРАХ ЛЕСІ УКРАЇНКИ

The category of coreference plays an important role in the creation of texts, as it is based on fundamental principles of text organization. The linearity of a text and the non-linearity of a situation described in the text are closely related to the mention of elements of a situation with another nomination of this object. Therefore, chains of coreferential names appear in the text and, having a binary relation, partly cause the emergence of a secondary nomination, which is an anaphoric relation with the primary nomination. The purpose of this article is to study the category of coreference in the poetic speech of Lesia Ukrainka based on the corpus of the Ukrainian language represented on mova.info. The object of the research is the idiostyle of Lesia Ukrainka’s poetry, and the subject is the functioning of the category of coreference. The categorical essence of coreference is characterized, the types of representatives of real objects, their functional and stylistic varieties and typical objects of representation in a poetic text are determined, and the most frequent structures are considered. Software for working with coreference has been developed and implemented. It has a user-friendly interface, which allows searching, sort, and doing the quantitative processing of the collected information according to the needs of researchers. On the material of 153 texts, 1520 referentially identical pairs are established. The coreferentiality index and the index of coreference coverage of the text proposed in this article helped to quantitatively assess the saturation of the text with the category of coreference. In addition, the classification of coreferential relations is composed: 1) identity; 2) collection gap; 3) part-whole; 4) predicative identity; 5) metonymy; 6) association. A dependency tree of a sentence made it possible to determine the location of coreferential pairs in the sentence, the position relative to each other, the belonging of the segment with the coreferent or the referent to the complicators of the sentence, i.e., a subordinate clause or phrases. This representation also helped to establish the type of syntactic relationship between the coreferent and the referent, the type of syntactic-semantic relationship between them, the presence of referent-dependent words, the intersection of the coreferent and the referent in gender and number. Automatic extraction of coreferential pairs from dependency trees allowed building intersentential coreference.

Recurrent synchronization network for emotion-cause pair extraction

Emotion-cause pair extraction is a fundamental task in emotion analysis, which aims to extract emotions and their corresponding causes from documents. Recent studies have employed two auxiliary tasks, namely emotion extraction and cause extraction, to facilitate the detection of emotion-cause pairs and their joint resolution in a multi-task learning framework. However, the implicit information-sharing mechanism in existing methods fails to fully utilize the rich interactive relations among these three tasks. In this study, we propose a recurrent synchronization network that explicitly models the interaction among different tasks. Our model performs multiple rounds of inference to detect emotions, causes, and emotion-cause pairs iteratively. Following each round of inference, the information from different modules is synchronized through explicit information transmission, allowing the three tasks to collaborate effectively. Extensive experiments demonstrate that our model can extract emotion-cause pairs more accurately, while significantly outperforming existing methods.

Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction

Inspired by quantum-like phenomena in human language understanding, recent studies propose quantum probability-inspired neural networks to model natural language by treating words as superposition states and a sentence as a mixed state. However, many complex natural language processing tasks (e.g., emotion–cause pair extraction or joint dialog act recognition and sentiment classification) require modeling the complex and graphical interaction of multiple text pieces (e.g., multiple clauses in a document or multiple utterances in a dialog). The existing quantum probability-inspired neural networks only encode sequential interaction of a sequence of words, but cannot model the complex interaction of text pieces. To generalize the quantum framework from modeling word sequence to modeling complex and graphical text interaction, we propose a Quantum Probability-inspired Graph Attention NeTwork (QPGAT) by combining quantum probability and graph attention mechanism in a unified framework. Specifically, a text interaction graph is firstly constructed to describe the complex interaction of text pieces. Then QPGAT models each text node as a particle in a superposition state and each node’s neighborhood in the graph as a mixed system in a mixed state to learn interaction-aware text node representations. We apply QPGAT to the two important and complex NLP tasks, emotion–cause pair extraction and joint dialog act recognition and sentiment classification. Experiment results show that QPGAT is competitive compared with the state-of-the-art methods on the two complex NLP tasks, demonstrating the effectiveness of QPGAT. Moreover, QPGAT can also provide a reasonable post-hoc explanation about the model decision process for emotion–cause pair extraction.

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results.

Optimizing emotion–cause pair extraction task by using mutual assistance single-task model, clause position information and semantic features

Optimizing emotion–cause pair extraction task by using mutual assistance single-task model, clause position information and semantic features

Order-guided deep neural network for emotion-cause pair prediction

Emotion-Cause Pair Extraction (ECPE) is a prediction task aiming to extract the emotions and their corresponding causes in a target document. The existing methods for this problem mainly focus on modeling the dependence between emotion clauses and related cause clauses and the interaction among emotion-cause pairs. However, these methods ignore the order information between emotion clauses and their cause clauses, which can be proved useful for the ECPE task. In this paper, we propose an order-guided deep predictive model, which integrates different orders between emotion clauses and their cause clauses into an end-to-end framework to tackle this task. Specifically, we build an order-guided clause encoder with a three-level long-short term memory (LSTM) network to learn the different orders from forward LSTM, backward LSTM and Bi-LSTM, respectively. In this way, the deep networks with different directions can effectively capture different orders, and therefore improve the performance of our model in this prediction task. Additionally, the previous methods use only a shared word encoder to capture word-level emotion and cause information, resulting in paying more attention to emotion information and lacking the ability to capture cause information. In order to overcome this deficiency, we design both an emotion-aware word encoder and a cause-aware word encoder to enhance the ability to capture the emotion and cause information. The experiment results illustrate that our method outperforms the other baselines on two real-world datasets, and demonstrate the effectiveness of the proposed method.

Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction

Aspect term extraction and opinion word extraction are two fundamental subtasks of aspect-based sentiment analysis. The internal relationship between aspect terms and opinion words is typically ignored, and information for the decision-making of buyers and sellers is insufficient. In this paper, we explore an aspect–opinion pair extraction (AOPE) task and propose a Question-Driven Span Labeling (QDSL) model to extract all the aspect–opinion pairs from user-generated reviews. Specifically, we divide the AOPE task into aspect term extraction (ATE) and aspect-specified opinion extraction (ASOE) subtasks; we first extract all the candidate aspect terms and then the corresponding opinion words given the aspect term. Unlike existing approaches that use the BIO-based tagging scheme for extraction, the QDSL model adopts a span-based tagging scheme and builds a question–answer-based machine-reading comprehension task for an effective aspect–opinion pair extraction. Extensive experiments conducted on three tasks (ATE, ASOE, and AOPE) on four benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches.

Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

Background/introductionConcept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. MethodsThis work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations. Additionally, a supervised a machine learning technique is implemented for performing concept-level sentiment analysis. Results and conclusionsExperimental results depict the effectiveness of the proposed system in terms of improved efficiency (P: 88%, R: 88%, F-score: 88%, and A: 87.5%).

Approach for CM/DM Current Extraction and Crosstalk Analysis of Twisted-Wire Pairs with Random Non-uniform Twisting

In this paper, a twisted-wire pairs (TWP) with random non-uniform twisting is established. It is divided into a complete pitch segment and a noncomplete pitch segment by the ratio between the pitch and the length. The randomness of the actual TWP cable is accurately simulated by the following methods: 1) random combination of complete pitch segments; 2) random combination of non-complete pitch segments; 3) random combination between 1) and 2). Based on the TWP model, an equivalent multi-conductor transmission lines (MTLs) model can be obtained. The neural network algorithm is introduced to describe the complex relationship between the arbitrary position of the TWP and the per-unit-length (p.u.l) parameter matrix. In addition, the crosstalk and the common-mode (CM) and differential-mode (DM) noise under field-to-wire coupling are predicted. The numerical results show that crosstalk and CM/DM noise in TWP cable are susceptible to the twisted pitch at high frequencies. Compared with full-wave simulation, the accuracy of the proposed method is proved.

Multiple perspective attention based on double BiLSTM for aspect and sentiment pair extract

Aspect category sentiment analysis has attracted increasing attention because of its outstanding performance in mining the fine-grained sentiment expression of users. In recent years, a new aspect category and sentiment pair extraction (ASPE) task has been proposed to simultaneously extract aspect categories and sentiment pairs. Most existing research works are designed in a two step pipeline, that is, they first perform aspect category detection, and subsequently conduct aspect category sentiment analysis. However, the pipeline method can clearly lead to error propagation from previous step. In this work, we propose a new framework for multiple perspective attention based on double BiLSTM with a novel joint strategy for ASPE to alleviate the accumulation of errors in the pipeline method. The experimental results on benchmark datasets SemEval and BDCI-2018 demonstrate the effectiveness of the proposed approach in terms of both accuracy and explainability for the ASPE task.

Time-bin entangled photon pairs from quantum dots embedded in a self-aligned cavity.

We introduce a scalable photonic platform that enables efficient generation of entangled photon pairs from a semiconductor quantum dot. Our system, which is based on a self-aligned quantum dot- micro-cavity structure, erases the need for complex steps of lithography and nanofabrication. We experimentally show collection efficiency of 0.17 combined with a Purcell enhancement of up to 1.7. We harness the potential of our device to generate photon pairs entangled in time bin, reaching a fidelity of 0.84(5) with the maximally entangled state. The achieved pair collection efficiency is 4 times larger than the state-of-the art for this application. The device, which theoretically supports pair extraction efficiencies of nearly 0.5 is a promising candidate for the implementation of bright sources of time-bin, polarization- and hyper entangled photon pairs in a straightforward manner.

A Mutually Auxiliary Multitask Model With Self-Distillation for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE), which aims to extract emotions and the corresponding causes in documents, has a wide range of applications in network public opinion analysis. Current two-stage methods first extract emotion and cause clauses, and then pair them. However, there are two problems in these methods: 1) the unidirectional enhancement between emotion and cause extraction fails to make full use of the correlation between them; 2) the errors from the first stage directly degrade the performance of the second stage. To address these problems, we firstly propose a mutually auxiliary multitask model to promote the extraction of emotion and cause clauses by adding two auxiliary tasks which are identical to the original tasks. The proposed model uses the predicted results generated by the two auxiliary tasks as extra features of each other’s main tasks, so as to establish the bidirectional correlation between emotion and cause extraction. Secondly, to reduce the influence of error propagation on the second stage, we design a self-distillation method for pairwise tasks to train the proposed model, which further improve the accuracy of emotion and cause extraction. Experimental results on the ECPE benchmark dataset show that the proposed model has achieved good performance on emotion-cause pair extraction, outperforming the baseline models by 1.92% in F1 score.