Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction (2020)

Abstract: Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in the pipeline. However, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios. To this end, we propose a novel tagging scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end fashion only with one unified grid tagging task. Additionally, we design an effective inference strategy on GTS to exploit mutual indication between different opinion factors for more accurate extractions. To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets. Extensive experimental results indicate that GTS models outperform strong baselines significantly and achieve state-of-the-art performance.

## introduction aspect-oriented fine-grained opinion extraction (afoe) aims to automatically extract opinion pairs (aspect term, opinion term) or opinion triplets (aspect term, opinion term, sentiment) from review text, which is an important task for fine-grained sentiment analysis @xcite . in this task, aspect term and opinion term are two key opinion factors. aspect term, also known as opinion target, is the word or phrase in a sentence representing feature or entity of products or services. opinion term refers to the term in a sentence * † corresponding author. sentence: the hot dogs are top notch and great coffee aspect terms: hot dogs, coffee opinion terms: top notch, great opinion pairs: (hot dogs, top notch), (coffee, great) opinion triplets: (hot dogs, top notch, positive), (coffee, great, positive) sentence: the hot dogs are top notch but average coffee aspect terms: opinion terms: opinion pairs: opinion triplets: hot dogs, coffee top notch, average (hot dogs, top notch), (coffee, average) (hot dogs, top notch, positive), (coffee, average, neutral) used to express attitudes or opinions explicitly. for example, in the sentence of figure 1 , "hot dogs" and "coffee" are two aspect terms, "top notch" and "average" are two opinion terms. to obtain the above two opinion factors, many works devote to the co-extraction of aspect term and opinion term in a joint framework @xcite @xcite @xcite . however, the extracted results of these works are two separate sets of aspect term and opinion term, and they neglect the pair relation between them, which is crucial for downstream sentiment analysis tasks and has many potential applications, such as providing sentiment clues for aspect level sentiment classification @xcite , generating fine-grained opinion summarization @xcite or analyzing in-depth opinions @xcite , etc. opinion pair extraction (ope) is to extract all opinion pairs from a sentence in the form of (aspect term, opinion term). an opinion pair consists of an aspect term and a corresponding opinion term. this task needs to extract three opinion factors, i.e., aspect terms, opinion terms, and the pair relation between them. figure 1 shows an example. we can see that the sentence "the hot dogs are top notch and great coffee!" contains two opinion pairs, respectively (hot dogs, top notch) and (coffee, average) (the former is the aspect term, and latter represents the corresponding opinion term). ope sometimes could be complicated because an aspect term may correspond to several opinion terms and vice versa. despite the great importance of ope, it is still under-investigated, and only a few early works mentioned or explored this task @xcite @xcite . by reviewing the aspect-based sentiment analysis (absa) @xcite ) research, we can summarize two types of state-of-the-art pipeline approaches to extract opinion pairs: (i). co-extraction @xcite +pair relation detection (pd) @xcite ; (ii). aspect term extraction (ae) @xcite +aspect-oriented opinion term extraction (aote) @xcite . nevertheless, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios. to address the above issues and facilitate the research of afoe, we propose a novel tagging scheme, grid tagging scheme (gts), which transforms opinion pair extraction into one unified grid tagging task. in this grid tagging task, we tag all word-pair relations and then decode all opinion pairs simultaneously with our proposed decoding method. accordingly, gts can extract all opinion factors of ope in one step, instead of pipelines. furthermore, different opinion factors are mutually dependent and indicative in the ope task. for example, if we know "average" is an opinion term in figure 1 , then "coffee" is probably deduced as an aspect term because "average" is its modifier. to exploit these potential bridges, we specially design an inference strategy in gts to yield more accurate opinion pairs. in the experiments, we implement three gts models, respectively, with cnn, lstm, and bert, to demonstrate the effectiveness and compatibility of gts. besides ope, we find that gts is very easily extended to aspect-oriented opinion triplet extraction (ote), by replacing the pair relation detection of ope with specific sentiment polarity detection. ote is a new fine-grained sentiment analysis task and aims to extract all opinion triplets (aspect term, opinion term, sentiment) from a sentence @xcite . to tackle the task, @xcite propose a two-stage framework and still extract opinion pair (aspect term, opinion term) in pipeline, thus suffering from error propagation. in contrast, gts can extract all opinion triplets simultaneously only with one unified grid tagging task. the main contributions of this work can be summarized as follows: the following sections are organized as follows. section 2 presents our proposed grid tagging scheme . in section 3, we introduce the models based on gts and the inference strategy. section 4 shows experiment results. section 5 and section 6 are respectively related work and conclusions. our code and data will be available at https://github.com/njunlp/gts . in this section, we first give the task definition of opinion pair extraction (ope) and opinion triplet extraction (ote), then explain how the two tasks are represented in grid tagging scheme. finally, we present how to decode opinion pairs or opinion triplets according to the tagging results in gts. ## validation models to verify the effectiveness and good compatibility of gts, we respectively tried three typical neural networks, i.e., cnn, lstm, and bert, as encoder implementations of gts (section 3.1). besides, different opinion factors in afoe mutually rely on and can benefit each other. therefore, we design an inference strategy to exploit these potential indications in section 3.2. figure 4 shows the overall architecture of gts models. ## related work in literature, only a few works mentioned or explored the opinion pair extraction. @xcite employ frequent pattern mining to extract aspect terms, then regard the closest adjective to aspect term as the corresponding opinion term. @xcite adopt dependency-tree based templates to identify opinion pairs after extracting the aspect term set and opinion term set. recently, some works adopt neural networks to perform the subtasks of ope, such as co-extraction of aspect term and opinion term @xcite @xcite . aspect term extraction @xcite , and aspect-oriented opinion term extraction @xcite , and finally combine them to accomplish ope in pipeline. to avoid the error propagation of pipeline methods, some studies use joint learning based on traditional machine learning algorithms and hand-crafted features, including imperatively defined factor graph (idf) @xcite , joint inference based on idf @xcite , and integer linear programming (ilp) @xcite . however, these methods heavily depend on the quality of handcrafted features and sometimes perform worse than pipeline methods @xcite . the opinion triplet extraction is a new aspectoriented fine-grained opinion extraction task @xcite . inspired by extracting (aspect term, sentiment) pair in a joint model @xcite @xcite , @xcite propose a two-stage framework to extract opinion triplets. in the first stage, they first use a neural model to extract the pair (aspect term, sentiment) and unpaired opinion terms, then detect the pair relation between aspect term and opinion terms in the second stage. we can see that the key opinon pair extraction of aspect term and opinion term is still accomplished in pipeline and their approach also suffers from error propagation.