To be Closer: Learning to Link up Aspects with Opinions (2021)

Abstract: Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA) (CITATION). However, the trees obtained from off-the-shelf dependency parsers are static, and could be sub-optimal in ABSA. This is because the syntactic trees are not designed for capturing the interactions between opinion words and aspect words. In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure. The aspect and opinion words are expected to be closer along such tree structure compared to the standard dependency parse tree. The learning process allows the tree structure to adaptively correlate the aspect and opinion words, enabling us to better identify the polarity in the ABSA task. We conduct experiments on five aspect-based sentiment datasets, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis demonstrates the average distance between aspect and opinion words are shortened by at least 19% on the standard SemEval Restaurant14 (CITATION) dataset.

## introduction aspect-based sentiment analysis (absa) @xcite aims at determining the sentiment polarity expressed towards a particular target in a sentence. for example, in the sentence "the battery life of this laptop is very long, but the price is too high", the sentiment expressed towards the aspect term "battery life" is positive, whereas the sentiment towards the aspect term "price" is negative. early research efforts @xcite @xcite @xcite loving the harry potter movie marathon... focus on using an attention mechanism @xcite to model interactions between aspect and context words. however, such attention-based models may suffer from overly focusing on the frequent words that express sentiment polarity while ignoring low-frequency ones @xcite . recent efforts show that the syntactic structures of sentences can facilitate the identification of sentiment features related to aspect words @xcite @xcite . nonetheless, these methods unfortunately suffer from two shortcomings. first, the trees obtained from off-the-shelf dependency parsers are static, and thus cannot adaptively model the complex relationship between multiple aspects and opinion words. second, an inaccurate parse tree could lead to error propagation downstream in the pipeline. several research groups have explored the above issues with a more refined parse tree. for example, @xcite constructed taskspecific structures by developing a gate mechanism to dynamically combine the parse tree information and a stochastic graph sampled from the hardkuma distribution @xcite . on the other hand, @xcite greedily reshaped the dependency parse tree by using manual rules to obtain the aspect-related syntactic structures. despite being able to effectively alleviate the tree representation problem, existing methods still depend on external parse trees, leading to one potential problem. the dependency parse trees are not designed for the purpose of absa but to express syntactic relations. specifically, the aspect term is usually a noun or a noun phrase, while the root of the dependency tree is often a verb or an adverb. according to statistics, for almost 90% 2 of the sentences, the roots of their dependency trees are not aspect words. such a root inconsistency issue may prevent the model from effectively capturing the relationships between opinion words and aspect words. for example, figure 1(a) shows the dependency tree obtained by the toolkit spacy 3 . the root is the gerund verb "loving" while the aspect term is the noun phrase "harry potter". the distance between the aspect words "harry" and "potter" and the critical opinion word "loving" under a dependency tree are four hops and three hops, respectively. however, their relative distances in the sequential order are two and three, respectively. intuitively, closer distance enables us to identify the polarity in the absa task better. figure 1(b ) shows an aspect-centric tree where the tree is rooted by the aspect words. the distances between aspect and opinion words are one hop and two hops, which is closer than the distance in the standard dependency parse tree. in this paper, we propose a model that learns aspect-centric latent trees (we name it as the aclt model) which are specifically tailored for the absa task. we assume that inducing tree structures whose roots are within aspect term enables the model to correlate the aspect and opinion words better. we built our model based on the structure attention mechanism @xcite and a variant of the matrix-tree theorem (mtt) @xcite . additionally, we proposed to impose a soft constraint to encourage the aspect words to serve as the root of the tree structure induced by mtt. as a result, the search space of inferring the root is reduced during the training process. our contributions are summarized as follows: to the best of our knowledge, we are the first to link up aspects with opinions through the specifically designed latent tree that imposes root constraints. ## model in this section, we present the proposed aspect-centric latent tree (aclt) model (figure 2 ) for the absa task. we first obtain the contextualized representations from the sentence encoder. next, we use a tree inducer to produce the distribution over all the possible latent trees. the underlying tree inducer is a latent-variable model which treats tree structures as the latent variable. once we have the distribution over the latent trees, we adopt the root refinement procedure to obtain aspect-centric latent trees. then, we can encode the probabilistic latent trees with a graph or tree encoder. finally, we use the structured representation from the tree encoder for sentiment classification. ## related work aspect-based sentiment analysis. early efforts on aspect-based sentiment focused on predicting polarity by employing attention mechanism @xcite to model interactions between aspect and context words @xcite @xcite @xcite . more recently, neural pretrained language models, for instance, bert @xcite enabled absa to achieve promising results. for example, @xcite manually constructed auxiliary sentences using the aspect word to convert absa into a sentencepair classification task. @xcite propagated opinion features from syntax neighborhood words to the aspect words, in a bert-based model. another line of work in absa focused on leveraging the explicit dependency parse trees to model the relationships between context and aspect words. @xcite and @xcite used gcns to integrate dependency tree information to capture structural and contextual information simultaneously for aspect-based sentiment analysis. @xcite greedily reshaped the dependency parse trees by using manual rules to obtain the task-specific syntactic structures. ## conclusion and future work in this paper, we propose to use aspect-centric latent trees (aclt) which are specifically tailored for the absa task to link up aspects with opinion words in an end-to-end fashion. experiments on five benchmark datasets show the effectiveness of our model. the qualitative and quantitative analysis illustrate that our model is able to improve the probability of aspect words becoming the root of the sentence by imposing root constraints. moreover, thorough analysis demonstrates our model shortens the average distances between aspect and opinions by at least 19% on the semeval restau-rant14 dataset. to the best of our knowledge, we are the first to link up aspects with opinions through the specifically designed latent tree that imposes root constraints. one possible future direction is to apply the proposed approach to other sentiment analysis tasks, such as aspect triplet extraction @xcite .