POIReviewQA: A Semantically Enriched POI Retrieval and Question Answering Dataset

by   Gengchen Mai, et al.

Many services that perform information retrieval for Points of Interest (POI) utilize a Lucene-based setup with spatial filtering. While this type of system is easy to implement it does not make use of semantics but relies on direct word matches between a query and reviews leading to a loss in both precision and recall. To study the challenging task of semantically enriching POIs from unstructured data in order to support open-domain search and question answering (QA), we introduce a new dataset POIReviewQA. It consists of 20k questions (e.g."is this restaurant dog friendly?") for 1022 Yelp business types. For each question we sampled 10 reviews, and annotated each sentence in the reviews whether it answers the question and what the corresponding answer is. To test a system's ability to understand the text we adopt an information retrieval evaluation by ranking all the review sentences for a question based on the likelihood that they answer this question. We build a Lucene-based baseline model, which achieves 77.0 achieves 79.2 challenging problem for future research by the GIR community. The result technology can help exploit the thematic content of web documents and social media for characterisation of locations.



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1. Introduction

Location-based services (LBS) and the underlying Point of Interest (POI) datasets play a increasingly important role in our daily interaction with mobile devices. Platforms such as Yelp, Foursquare, Google Map allow users to search nearby POIs based on their names, place types, or tags, which requires manual data annotation. In fact, besides these structured data, POIs are typically associated with abundant unstructured data such as descriptions and users’ reviews which contain useful information for search and question answering purpose. For example questions like “Is this restaurant dog friendly?” or “Is this night club 18+?” can be answered by relevant text in reviews such as “Great dog friendly restaurant” or “18+ night club”. This information can also help accomplishing search needs such as “find night clubs near me which are 18+”.

There are only a few existing GIR benchmark datasets (e.g., GeoCLEF (Mandl et al., 2008)) and they often lack in rich annotations as would be required for the examples above. Recently many datasets have been produced for reading comprehension such as SQuAD (Rajpurkar et al., 2016). However, they do not have a spatial/platial component. Here we present a POI search and question answering dataset called POIReviewQA with detail annotations of context and answers. Baseline models are implemented to demonstrate the difficulty of this task.

Our work provides an evaluation benchmark for geographic information retrieval and question answering systems. It follows the idea of semantic signatures for social sensing  (Janowicz et al., 2018) by which we can study POI types using patterns extracted from human behavior, e.g., what people write about places of a particular type. Intuitively, questions about age limits only arise in the narrow context of a few such types, e.g., nightclubs, movie theaters, and so on. Furthermore, unstructured data such as reviews are often geo-indicative without the need for explicit geographic coordinates. For instance, people may be searching for a central but quiet hotel (Janowicz et al., 2008). It is those questions that we will address in the following.

2. The POIReviewQA Task

We created POIReviewQA based on the Yelp Challenge 11 (YC11) dataset222https://www.yelp.com/dataset/challenge and the QA section of POI pages.

Query Set Generation

We create the question answer dataset from the “Ask the Community” section333https://www.yelpblog.com/2017/02/qa of POI pages. The Yelp platform is dominated by popular business types such as restaurants. In order to produce a balanced query set for all business types we performed stratified sampling: 1) count the frequencies of POI name suffixes (single words) in YC11; 2) for every suffix with at least frequency 10 we create a quoted search query restricting to the Yelp business QA domain444Search “site:https://www.yelp.com/questions/ ‘Restaurant” via Google, and collect community QA page URLs from Google search engine; 3) collect questions and answers from the community QA pages. In total, 1,701 quoted search queries results are collected from Google with up to 100 search results for each query. Since the last term often indicates the place type of a POI, the collected Yelp business question pages have a wide coverage of different place types. In total 20K questions were collected from Yelp business question pages for 1022 Yelp business types. Each question is associated with one or multiple POIs with several POI types, e.g., Echoplex (Music Venues, Bars, Dance Clubs) or Paper Tiger Bar (Cocktail Bars, Lounges).

Relevance and Answer Annotation

For each question, 10 review candidates are selected by stratified sampling from the search result of a lucene-based setup, i.e., applying Elastic Search to POI reviews based on the question with constraint to the associated POI types. We developed a crowd-facing Web server and deployed it on Amazon Mechanical Turk to let raters annotate each sentence of these 10 reviews with respect to whether it answer the current question and what the corresponding answer is. The annotation results are collected for each question. To date, we have collected about 4100 questions. Basic statistic for these are shown in Tab. 1. In order to study the relationship between raters (given 3 raters per review sentence) and the accuracy of the raters, we divide the sentences into 4 sets based on the number of raters that agreed on each sentence, denoted as , , , . Then we randomly sample 20 sentences from each of the last three sets (, , ). By manually inspecting the relevance of these sentences to the corresponding questions. The resulting accuracy of each sample set is 45% for , i.e., 9/20 sentences, 90% for , 100% for . We treat the sentences in , as relevant, and the rest are labeled as irrelevant sentences. These labels are used to evaluate different models.

Evaluation Metrics

Area under curve (AUC) and mean average percision (MAP) are used as evaluation metrics.

# of Annotated question 4,100
% of questions WITHOUT related reviews 11.4%
Avg. # of related reviews per question 4.61
Avg. # of 1 rater agreeing on relevant sentence per question 2.19
Avg. # of 2 raters agreeing on relevant sentence per question 1.08
Avg. # of 3 raters agreeing on relevant sentence per question 0.83
Table 1. The Statistic of POIReviewQA
Reason Example Ranking (H/L/E)
Paraphrase Q: About how long should I expect my visit to be?
T: Venues & Event Spaces; Kids Activities 1/107/88
C: We were there for about 2 hours, including the show. out of 158
A: took 2 hrs
Hyponym Q: Any good vegan choices?
T: RestaurantsCajun/Creole 2/49/18
Sent: After scanning the menu for a bit however, I was able to find the tofu wings. out of 83
A: Tofu wings could be a choice
Synonymy Q: Any recommendations on how to score a table? …
T: RestaurantsFrench 1/63/14
C: I made a reservation a day in advance thinking it will be busy. out of 98
A: A day in advance
Deduction Q: Are there classes for seniors?
T: Art Galleries; Art Schools 1/60/15
C: Great studio for all, including kids! out of 72
A: There are classes for seniors
Common Sense Q: Do they buy comic books?
T: ShoppingComic Books 1/45/53
C: The concerns: The store currently has no consignment or new issues. out of 62
A: No
Table 2. Examples of POIReviewQA. Each example consists of a question Q, one or more POI types T, a context sentence C from the POI reviews, and an answer A. The ranking of sentence (C) based on human judgements (H), Lucene (L), and sentence embedding (E) is also shown.

3. Experiment with Baseline Models

In order to provide a similar search functionality to Yelp’s new review-based POI search555https://engineeringblog.yelp.com/2017/06/moving-yelps-core-business-search-to-elasticsearch.html, we developed a TF-IDF based model to search through all sentences from 10 reviews based on a question. An evaluation using the POIReviewQA dataset gives 77% AUC and 48.8% MAP. We also applied the sentence embedding model proposed by Sanjeev Arora et al.  (Arora et al., 2017)

. It improves the average word embeddings using SVD and gives what the authors call “tough-to-beat” results for a text similarity tasks. We use the pretrained Google News 300 dimension Word2Vec embeddings to generate the sentence level embedding for both questions and review sentences. Then their cosine similarities are used to rank the sentences given a question. Evaluation by POIReviewQA gives 79.2% AUC and 41.0% MAP. Comparing to the TF-IDF model, the sentence embedding-based model gives a higher AUC (which is sensitive to overall rankings) but lower MAP (which is sensitive to top rankings). The results from both baseline models indicate that the POIReviewQA dataset presents a challenging task. Table

2 shows examples for which the baseline model fails. Correctly predicting relevant sentence requires an understanding of language and common sense. We hope that the dataset will enable further GIR research about question answering as it relates to place types.


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  • Janowicz et al. (2018) Krzysztof Janowicz, Grant McKenzie, Yingjie Hu, Rui Zhu, and Song Gao. 2018. Using Semantic Signatures for Social Sensing in Urban Environments. In Mobility Patterns, Big Data and Transport Analytics.
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