Positionless aspect based sentiment analysis using attention mechanism.

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ATP: A holistic attention integrated approach to enhance ABSA

Aspect based sentiment analysis (ABSA) deals with the identification of the sentiment polarity of a review sentence towards a given aspect. Deep Learning sequential models like RNN, LSTM, and GRU are current state-of-the-art methods for inferring the sentiment polarity. These methods work well to capture the contextual relationship between the words of a review sentence. However, these methods are insignificant in capturing long-term dependencies. Attention mechanism plays a significant role by focusing only on the most crucial part of the sentence. In the case of ABSA, aspect position plays a vital role. Words near to aspect contribute more while determining the sentiment towards the aspect. Therefore, we propose a method that captures the position based information using dependency parsing tree and helps attention mechanism. Using this type of position information over a simple word-distance-based position enhances the deep learning model's performance. We performed the experiments on SemEval'14 dataset to demonstrate the effect of dependency parsing relation-based attention for ABSA

토큰 단위 분류모델을 위한 중요 토큰 포착 및 시퀀스 인코더 설계 방법

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 정교민.With the development of internet, a great of volume of data have accumulated over time. Therefore, dealing long sequential data can become a core problem in web services. For example, streaming services such as YouTube, Netflx and Tictoc have used the user's viewing history sequence to recommend videos that users may like. Such systems have replaced the user's viewed video with each item or token to predict what item or token will be viewed next. These tasks have been defined as Token-Level Classification (TLC) tasks. Given the sequence of tokens, TLC identifies the labels of tokens in the required portion of this sequence. As mentioned above, TLC can be applied to various recommendation Systems. In addition, most of Natural Language Processing (NLP) tasks can also be formulated as TLC problem. For example, sentence and each word within the sentence can be expressed as token-level sequence. In particular, in the case of information extraction, it can be changed to a TLC task that distinguishes whether a specific word span in the sentence is information. The characteristics of TLC datasets are that they are very sparse and long. Therefore, it is a very important problem to extract only important information from the sequences and properly encode them. In this thesis, we propose the method to solve the two academic questions of TLC in Recommendation Systems and information extraction: 1) How to capture important tokens from the token sequence and 2) How to encode a token sequence into model. As deep neural networks (DNNs) have shown outstanding performance in various web application tasks, we design the RNN and Transformer-based model for recommendation systems, and information extractions. In this dissertation, we propose novel models that can extract important tokens for recommendation systems and information extraction systems. In recommendation systems, we design a BART-based system that can capture important portion of token sequence through self-attention mechanisms and consider both bidirectional and left-to-right directional information. In information systems, we present relation network-based models to focus important parts such as opinion target and neighbor words.인터넷의 발달로, 많은 양의 데이터가 시간이 지남에 따라 축적되었다. 이로인해 긴 순차적 데이터를 처리하는 것은 웹 서비스의 핵심 문제가 되었다. 예를 들어, 유튜브, 넷플릭스, 틱톡과 같은 스트리밍 서비스는 사용자의 시청 기록 시퀀스를 사용하여 사용자가 좋아할 만한 비디오를 추천한다. 이러한 시스템은 다음에 어떤 항목이나 토큰을 볼 것인지를 예측하기 위해 사용자가 본 비디오를 각 항목 또는 토큰으로 대체하여 사용할 수 있다. 이러한 작업은 토큰 수준 분류(TLC) 작업으로 정의한다. 토큰 시퀀스가 주어지면, TLC는 이 시퀀스의 필요한 부분에서 토큰의 라벨을 식별한다. 이렇게와 같이, TLC는 다양한 추천 시스템에 적용될 수 있다. 또한 대부분의 자연어 처리(NLP) 작업은 TLC 문제로 공식화될 수 있다. 예를 들어, 문장과 문장 내의 각 단어는 토큰 레벨 시퀀스로 표현될 수 있다. 특히 정보 추출의 경우 문장의 특정 단어 간격이 정보인지 여부를 구분하는 TLC 작업으로 바뀔 수 있다. TLC 데이터 세트의 특징은 매우 희박(Sparse)하고 길다는 것이다. 따라서 시퀀스에서 중요한 정보만 추출하여 적절히 인코딩하는 것은 매우 중요한 문제이다. 본 논문에서는 권장 시스템과 정보 추출에서 TLC의 두 가지 학문적 질문- 1) 토큰 시퀀스에서 중요한 토큰을 캡처하는 방법 및 2) 토큰 시퀀스를 모델로 인코딩하는 방법 을 해결하는 방법을 제안한다. 심층 신경망(DNN)이 다양한 웹 애플리케이션 작업에서 뛰어난 성능을 보여 왔기 때문에 추천 시스템 및 정보 추출을 위한 RNN 및 트랜스포머 기반 모델을 설계한다. 먼저 우리는 자기 주의 메커니즘을 통해 토큰 시퀀스의 중요한 부분을 포착하고 양방향 및 좌우 방향 정보를 모두 고려할 수 있는 BART 기반 추천 시스템을 설계한다. 정보 시스템에서, 우리는 의견 대상과 이웃 단어와 같은 중요한 부분에 초점을 맞추기 위해 관계 네트워크 기반 모델을 제시한다.1. Introduction 1 2. Token-level Classification in Recommendation Systems 8 2.1 Overview 8 2.2 Hierarchical RNN-based Recommendation Systems 19 2.3 Entangled Bidirectional Encoder to Auto-regressive Decoder for Sequential Recommendation 27 3. Token-level Classification in Information Extraction 39 3.1 Overview 39 3.2 RABERT: Relation-Aware BERT for Target-Oriented Opinion Words Extraction 49 3.3 Gated Relational Target-aware Encoder and Local Context-aware Decoder for Target-oriented Opinion Words Extraction 58 4. Conclusion 79박

Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow finer-grained inferences about sentiment to be drawn from the same text, depending on context. For example, a given text can have different targets (e.g., neighborhoods) and different aspects (e.g., price or safety), with different sentiment associated with each target-aspect pair. In this paper, we investigate whether adding context to self-attention models improves performance on (T)ABSA. We propose two variants of Context-Guided BERT (CG-BERT) that learn to distribute attention under different contexts. We first adapt a context-aware Transformer to produce a CG-BERT that uses context-guided softmax-attention. Next, we propose an improved Quasi-Attention CG-BERT model that learns a compositional attention that supports subtractive attention. We train both models with pretrained BERT on two (T)ABSA datasets: SentiHood and SemEval-2014 (Task 4). Both models achieve new state-of-the-art results with our QACG-BERT model having the best performance. Furthermore, we provide analyses of the impact of context in the our proposed models. Our work provides more evidence for the utility of adding context-dependencies to pretrained self-attention-based language models for context-based natural language tasks

Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging attention mechanism to extract opinion words for different aspects. However, a persistent challenge is the effective management of semantic mismatches, which stem from attention mechanisms that fall short in adequately aligning opinions words with their corresponding aspect in multi-aspect sentences. To address this issue, we propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. Specifically, we first introduce a neighboring span enhanced module which highlights various compositions of neighboring words and given aspects. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Extensive experiments on three benchmark datasets demonstrate that our model achieves state-of-the-art results. The source code is available at https://github.com/AONE-NLP/ABSA-AOAN.Comment: 8 pages, 5 figure, ECAI 202