Semantic Context Path Labeling for Semantic Exploration of User Reviews (2021)

Abstract: In this paper we present a prototype demonstrator showcasing a novel method to perform semantic exploration of user reviews. The system enables effective navigation in a rich contextual semantic schema with a large number of structured classes indicating relevant information. In order to identify instances of the structured classes in the reviews, we defined a new Information Extraction task called Semantic Context Path (SCP) labeling, which simultaneously assigns types and semantic roles to entity mentions. Reviews can rapidly be explored based on the fine-grained and structured semantic classes. As a proof-of-concept, we have implemented this system for reviews on Points-of-Interest, in English and Korean.

## introduction in this paper, we demonstrate a system that proposes a novel approach for extracting rich and diverse information from informative texts, and allowing effective semantic navigation among the extracted categories. our system is specifically designed for exploring large quantities of user reviews, which contain a wealth of useful information for potential new users, difficult to exploit in existing review platforms. the users of our system can navigate in a rich semantic schema representing a large number of hierarchically structured information categories relevant in the reviews, where top classes have multiple layers of subclasses and attribute classes. the semantic navigation can start from the top classes, or directly from the attribute classes. mentions of the structured classes in the reviews are automatically assigned labels that we call semantic context paths (scps), which denote their types and semantic roles. by semantic roles, we mean classes for which the mentions instantiate attributes. for example, in the sentence "see a good movie for @xmath05 is assigned the scp label showandexhibition.payment.pricevalue, which means that it is the price of a movie show. we implemented a supervised scp tagging system for poi reviews: (1) we created an scp semantic schema for the domain; (2) we labeled datasets both for english and korean using an annotation tool that we developed for this task; and (3) we developed and evaluated models for this ie task. we demonstrate the novel exploration method based on structured semantic classes through a dedicated exploration interface. it is not targeted at end-users at this stage: our purpose is to showcase the new approach in an intuitive way. in the following sections, we first expose the advantages of exploring user reviews through semantic context paths compared to the methods used in existing social media review sites (section 2), we then describe the scp labeling task (section 3) and finally present our scp tagging system on poi reviews. we show that performing sequence labeling relying on a state-of-the-art contextual language model extracts with high accuracy great numbers of scps from user reviews in multiple languages (section 4). ## semantic exploration of user reviews popular poi review sites like google maps or tripadvisor provide their users with multiple methods for obtaining information on pois. in this section, we briefly describe the navigation methods offered by existing major user review sites on pois, and compare them with navigation based on scps. the most widespread way to explore reviews is based on pre-selected frequent terms, as shown in figure 1 . the user can click on the terms, and thus rapidly access the reviews containing them. however, the main limitation is that the frequent terms are not grouped semantically and thus similar information is handled separately (c.f. jacket and sweater). in addition, important information can be conveyed by clausal expressions like dress accordingly, and therefore be missed by term detection algorithms. finally, in many cases, when talking about relevant topics, like prices, booking, or the availability of food on-site, linguistic expression is so heterogeneous that a method based on term frequency will not detect them. keyword search is another way to explore reviews, as shown in figure 2 . its advantage over the previous method is that the user is not limited by a pre-selected set of terms. however it is slower, since the user needs to enter queries, and getting all the relevant information may require multiple queries. moreover, some queries will fail either because the information is absent or because the query keywords are not matched, which can be frustrating due to the waste of time. finally, a few sites propose pre-selected flat categories for navigation, as illustrated in figure 3 . this method provides fast access to information by clicking on the categories, and improves preselected term navigation. however, since the categories are not structured, they can be ambiguous. e.g. "price" merges all prices: admission price, price of on-site food, or drink, price of a specific onsite activity, etc. taken together, keyword based navigation lacks the generality offered by structured search, however currently proposed structured search does not disambiguate general categories. compared to these methods, the advantage of navigation based on semantic context paths is that it provides rapid and effective access to precise information on a great diversity of topics. (3) context-dependent semantic roles specifying the classes for which the mentions instantiate attributes. as an example, assume we want to extract various kinds of useful information from point-of-interest reviews about recreation places (e.g. movie theaters), such as food, prices, visitors, etc. figure 4 illustrates how scp labeling is applied on a real poi comment. the proposed ie task goes beyond traditional named entity recognition (ner), which merely assigns semantic categories to entity mentions @xcite , even when the set of categories is fine-grained and hierarchical @xcite . in contrast, scp labels also contain information on the semantic content of the entities or concepts within the mentions, but at the same time also on their semantic roles and types of relations within the text. in the example in figure 4 , good price does not have a simple priceproperty label as it could have in a classical ner system, but showandexhibition.priceproperty, which indicates that it is related to mentions with the same root label, showandexhibition -in this example regency theatres and movie, which are respectively tagged as showan-dexhibition.loc, and showandexhibition.subject. scp tagging therefore presents the advantage of extracting rich contextual information, while still being a sequence labeling task. ## system description the semantic navigation system has two main components: the ie component, which identifies mentions and labels them with scps off-line, and a navigation component, which allows exploring the reviews based on the structured classes. we describe these two components in the following subsections. ## conclusion we have presented a new method for the semantic exploration of user reviews. the system underlying the demonstrator relies on an ie component which identifies relevant mentions in the reviews and labels them with semantic context paths, denoting their types and semantic roles. we have implemented an scp tagger that extracts information from user reviews on pointsof-interest. we have defined a dedicated semantic schema, created datasets, and developed sequence labeling models for the task. the ie component was quantitatively evaluated on english and korean, and showed promising qualitative results on other languages. we have designed a review exploration interface exploiting the output of the scp tagger. a sunburst chart in the interface allows navigation among the classes, and rapid access to relevant information in the reviews. future work includes integrating a wider range of languages to the review exploration interface.