Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (2020)

Abstract: Aspect extraction is a widely researched field of natural language processing in which aspects are identified from the text as a means for information. For example, in aspect-based sentiment analysis (ABSA), aspects need to be first identified. Previous studies have introduced various approaches to increasing accuracy, although leaving room for further improvement. In a practical situation where the examined dataset is lacking labels, to fine-tune the process a novel unsupervised approach is proposed, combining a lexical rule-based approach with coreference resolution. The model increases accuracy through the recognition and removal of coreferring aspects. Experimental evaluations are performed on two benchmark datasets, demonstrating the greater performance of our approach to extracting coherent aspects through outperforming the baseline approaches.

## introduction aspect-based sentiment analysis (absa) is a task involving the identification of key terms (words and phrases) that refer to important parts, features, attributes, or properties of a targeted product, object or service, along with associated sentimental emotions, opinions or evaluations. what started out as a simple document-level classification task @xcite , i.e., using reviews to differentiate positive from negative, has evolved into a heavily researched field of natural language processing and information retrieval @xcite . as social presence becomes more standard, the need for detecting opinions in comments or reviews becomes more present. due to the multi-perspective opinion-oriented nature of the comments, this task will require sentence or phrase-level aspect extraction. the system must be able to locate the expressions of aspects on a sentence-level, for example in the following examples, the aspects and their associated sentiment are clear; seaweed and chewy, and coronavirus and hate, terrible respectively: "the seaweed was too chewy", and "hate it, the coronavirus is terrible". the existing approaches for extracting aspect are in two branches: supervised and unsupervised. supervised approaches often formulate ate as a token-level sequence labeling problem, achieving better accuracy than unsupervised methods in general @xcite @xcite . however, these approaches generally require annotated data and can run into domain adaptation issues. moreover, in reality human labelling is a time-consuming and laborious work, motivating the unsupervised approach. topic model based approaches were proposed for this purpose @xcite . these approaches model the text corpus as a mixture of opinion topics, treating the task as a problem in topic coreference resolution. this process labels aspects relating to the extracted opinion topic while dealing with coreferring aspects @xcite @xcite . although the aspects interpreted by these models express a corpus well, they aren't coherent; individual aspects are of low quality, consisting of irrelevant or distantly-related concepts. the work in @xcite ) first proposed a manually-defined rulebased approach to extract product features through observing frequent nouns and noun chunks. this approach sparked the development of numerous approaches based on frequent term mining and dependency parsing @xcite . later, the work in @xcite proposed a unique approach to learn syntactic relations using dependency trees. although innovative, the rule-based models heavily relied on predefined rules which only worked well when the aspect terms are confined to a small group of nouns. in our project, we target the issue of conducting aspect-based sentiment analysis when there is the lack of labelled data, which presents a practical challenge. to this end, as a starting point, we propose an unsupervised approach for aspect extraction on the data corpus, forming the foundation of our following works. we particularly seek an advanced rule-based approach due to its efficiency and independence from manual efforts. we first extract candidate aspects using dependency parsing and coreference resolution. a careful selection process is then applied using unsupervised techniques; inspecting the candidates for duplicate and incorrect aspects. specifically, syntactic rules are applied on the part of speech (pos) and dependency information of a document to convert it into a candidate aspect list. this candidate list is then reduced to a final list by first applying coreference resolution, removing candidates that refer to an already existing aspect to avoid duplicity. finally, an unsupervised filtering technique is applied on the candidates, calculating the cosine similarity of an aspect's word embedding to its neighbours and removing those that don't meet an optimal threshold. overall, our proposed approach consists of several stages where in each the candidate list is reduced. this allows our model to overcome the small noun group restraint by first taking in a broad list of noun phrases. a clustering process is applied to complete the categorisation task to an extent. ## methodology the workflow of our proposed unsupervised aspect extraction method can be broken down into four sub-processes: i) pre-processing and text handling; ii) noun chunk extraction via dependency parsing; iii) candidate extraction using rules and coreference resolution; and iv) aspect term refinement. ## experiments and results in this section, we report our evaluations of the proposed approach on two benchmark datasets. ## conclusions and future work we have proposed and implemented an approach to aspect extraction utilising an unsupervised rulebased coreference resolution model. the basis of this approach is to apply a rule-based checking system on noun chunks extracted from the text. what started as a simple model has proven itself to be a valid approach, outperforming previous similarly unsupervised approaches. additionally, the clusters produced on each aspect's word vector are coherent to a satisfactory level, reflecting the eligibility of our baseline model. to improve the purging process, word vectors can be learned for a much larger vocabulary. if this can be implemented, foreign dish words such as rasamalai won't be incorrectly ruled out as aspects due to them not being in the vocabulary. slang in-terpretations such as rule in "the food options rule!" can be investigated by using a similar technique to the stop word list. we will also involve machine learning techniques to improve the rule-based approach. through training our model with the output of rules as an indicator feature for a discriminative learning model, we can expect that our rules are fine-tuned and adaptable to different corpora. furthermore, to avoid mistakes in clustering where similar words included in different categories are graphed in similar locations, additional learning can be acquired by our model. extra checks can be performed once a certain black-listed word is found in an aspect, and word embeddings can be trained further. in addition, we will perform sentiment analysis on the extracted aspects and investigate whether public sentiment can reflect the real-estate prices.