CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews Hu Xu, University of Illinois at Chicago Sihong Xie, Lehigh University Lei Shu, University of Illinois at Chicago Philip S. Yu, University of Illinois at Chicago, Tsinghua University BigData ’16
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CER: Complementary Entity Recognitionhxu/bigdata2016_slides.pdf · Complementary Entity Recognition (CER) • Extract complementary entities from sentences of reviews. (The target
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CER: Complementary Entity Recognition
via Knowledge Expansion on Large Unlabeled Product Reviews
Hu Xu, University of Illinois at Chicago Sihong Xie, Lehigh University Lei Shu, University of Illinois at Chicago Philip S. Yu, University of Illinois at Chicago, Tsinghua University
BigData ’16
My Black Friday
Experience
I have a case and want to add a new GPU.
This is how a compatible GPU should look like.
I found a 1070 GPU with a good price on Newegg.
Unfortunately, the GPU is too long to fit in… and non-refundable.
In the end, I damaged my case a little…
What’s a better way to avoid this?
So we need to identify the fact that the GPU does not like some cases…
• Knowledge Expansion on a Large Amount of Reviews
• Experiments
• Conclusions
Sentiment Analysis on Reviews (Liu, 2012)
• Product reviews contain a huge amount of information about first-hand user experiences in a sadly unstructured text format.
• Aspect-level sentiment analysis on product reviews is a key task to understand customers’ opinions on opinion targets: products and aspects (features) of products.
• We focus on complementary entities in reviews.
!
What’s an Entity?• Something that has separate and distinct existence and
objective or conceptual reality.
• —Merriam-Webster
• We are interested in entities related to products.
• Named Entity
• e.g., Samsung Galaxy S6, Microsoft Surface
• General Entity
• tablet, cellphone, computer, etc.
What’s a Complementary Entity?• Customers also express their opinions on a relation between a reviewed
product and another product.
• One relation type is complementary relation: two products (entities) should work together.
• Definition:
• target entity: the reviewed product;
• complementary entity: the related product in a complementary relation.!
• Example:
• This card works with my phone.
A Few Examples
A Few Examples with Opinions
Complementary Entity Recognition (CER)
• Extract complementary entities from sentences of reviews. (The target entities can be obtained from product titles of the reviewed product)
• e.g., extract phone from “It works with my phone.”
• Differences from Named Entity Recognition (NER):
• Including general entities: e.g., case, phone.
• Context dependent
• e.g., “It works with my iPhone 7” vs “I like my iPhone 7.”
• We only focus on CER in this paper since sentiment classification is an independent task and requires different techniques.
• Knowledge Expansion on a Large Amount of Reviews
• Experiments
• Conclusions
Conclusions• We introduce a novel task Complementary Entity
Recognition (CER) and an unsupervised method for recognition.
• We utilize big data to expand domain knowledge and use the domain knowledge to improve the performance of recognition.
• Future works can be
• sentiment classification for complementary entities;
• automatic knowledge accumulation from data.
Q&A• The annotated dataset can be found at:
• https://www.cs.uic.edu/~hxu/CER_dataset.html
• For details, please go for the original paper:
• Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu, CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews, IEEE International Conference on Big Data 2016, Washington D.C., Dec 5-8, 2016.