PDF] Unsupervised Synonym Extraction for Document Enhancement in E
Por um escritor misterioso
Last updated 08 julho 2024
![PDF] Unsupervised Synonym Extraction for Document Enhancement in E](https://d3i71xaburhd42.cloudfront.net/cc69e457a8f15cea43f3c6ae3464bec4450613dd/3-Figure2-1.png)
A two-phase unsupervised synonym discovery framework is proposed for extracting synonym rules from the search log data and it is demonstrated that 85.5% of the extracted rules are high quality pairs. The vocabulary gap between search queries and product descriptions is an important problem in modern e-commerce search engines. Most of the existing methods deal with the vocabulary gap issues by rewriting user-input queries. In this work, we describe another way to address vocabulary gap issues in the e-commerce search systems. In particular, we propose an unsupervised synonym extraction framework for document enhancement. Comparing to existing methods, the purposed framework has two main differences: 1) instead of extending and rewriting user-input queries, we make the synonym tokens searchable by adding them into the text descriptions of products; 2) the whole process is unsupervised and doesn’t require any human labels. A two-phase unsupervised synonym discovery framework is proposed for extracting synonym rules from the search log data. We demonstrate the effectiveness of our approach by two experiments: 1) we do online A/B testing experiments in multiple countries, which show significant improvements in key business metrics; 2) we conduct a human audit to evaluate the quality of the extracted synonym rules, which indicates that 85.5% of the extracted rules are high quality pairs.
Improving extractive document summarization with sentence centrality
![PDF] Unsupervised Synonym Extraction for Document Enhancement in E](https://media.springernature.com/m685/springer-static/image/art%3A10.1007%2Fs10462-022-10144-1/MediaObjects/10462_2022_10144_Fig3_HTML.png)
A survey on sentiment analysis methods, applications, and challenges
![PDF] Unsupervised Synonym Extraction for Document Enhancement in E](https://onlinelibrary.wiley.com/cms/asset/c092a95f-f405-4f0b-9caa-64c0f9ae66e4/9781118574560.biblio.fp.png)
Bibliography - Knowledge Needs and Information Extraction - Wiley Online Library
![PDF] Unsupervised Synonym Extraction for Document Enhancement in E](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41598-021-91189-0/MediaObjects/41598_2021_91189_Fig1_HTML.png)
Research on automatic labeling of imbalanced texts of customer complaints based on text enhancement and layer-by-layer semantic matching
![PDF] Unsupervised Synonym Extraction for Document Enhancement in E](https://www.altexsoft.com/static/blog-post/2023/11/2e4e9811-d133-4e5c-8e92-f7445834b3ae.jpg)
Natural Language Processing: Use Cases, Approaches, Tools
![PDF] Unsupervised Synonym Extraction for Document Enhancement in E](https://aclanthology.org/thumb/2022.findings-acl.34.jpg)
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction - ACL Anthology
![PDF] Unsupervised Synonym Extraction for Document Enhancement in E](https://ars.els-cdn.com/content/image/1-s2.0-S095741742301391X-gr3.jpg)
General-use unsupervised keyword extraction model for keyword analysis - ScienceDirect
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PDF Extract API Adobe PDF Services
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Efficient unsupervised keywords extraction using graphs
Data Analytics and AI, PDF, Machine Learning
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Extracting cancer concepts from clinical notes using natural language processing: a systematic review, BMC Bioinformatics
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Mining opinionated product features using WordNet lexicographer files - Saif A. Ahmad Alrababah, Keng Hoon Gan, Tien-Ping Tan, 2017
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PDF) Search with Synonyms: Problems and Solutions
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Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
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