Spelling correction systems for e-commerce platforms
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This document discusses spell correction systems, emphasizing their importance for e-commerce by enhancing user queries and revenue while minimizing bounce rates. The authors, Anjan Goswami and Huizhong Duan, explore challenges in developing these systems, including modeling errors and candidate generation, and present strategies based on machine learning and natural language processing. The paper also details evaluation metrics, such as accuracy and business impact, to assess spell correction effectiveness in e-commerce settings.
![The Spell correction problem
Rich literature [KCG90, Pet80].
Active research area [CB04].
Combination of NLP, Machine Learning [DH11, BB01, LDZ12] and
Systems problems [Kuk92].
Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 2 / 31](https://image.slidesharecdn.com/spellnew-160317033138/85/Spelling-correction-systems-for-e-commerce-platforms-2-320.jpg)
![Error statistics
Approximately 26% queries have spelling error in web queries [JM].
E-com data can be expected to be similar.
Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 11 / 31](https://image.slidesharecdn.com/spellnew-160317033138/85/Spelling-correction-systems-for-e-commerce-platforms-11-320.jpg)
Spelling correction systems for e-commerce platforms
- 1. Spell Correction Systems for E-commerce engines Anjan Goswami HuiZhong Duan Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 1 / 31
- 2. The Spell correction problem Rich literature [KCG90, Pet80]. Active research area [CB04]. Combination of NLP, Machine Learning [DH11, BB01, LDZ12] and Systems problems [Kuk92]. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 2 / 31
- 3. Spell correction for e-commerce Critical site feature for e-commerce. Impact of ML based spell correction Adds revenue. Reduces bounce rate. Reduces null Results. Departments such as pharmacy can have huge gain in revenue with Spell Correction. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 3 / 31
- 4. Spell correction for e-commerce Science part is same as any other large scale spell correction systems. Demand and supply side corpus. Conversion focus. User Interfaces. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 4 / 31
- 5. Spell correction Evaluation Accuracy for misspelled queries. Accuracy for correctly spelled queries. Business metrics. Coverage. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 5 / 31
- 6. The problem Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 6 / 31
- 7. The problem Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 7 / 31
- 8. The problem Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 8 / 31
- 9. The problem Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 9 / 31
- 10. The problem Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 10 / 31
- 11. Error statistics Approximately 26% queries have spelling error in web queries [JM]. E-com data can be expected to be similar. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 11 / 31
- 12. Error Types Typographic errors: Covr ← Cover Cognitive errors: Visio Tv ← Vizio Tv Non-english word errors: X345678 ← X345677 Contextual errors: life of Pie ← Life of Pi Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 12 / 31
- 13. Challenges General Challenges Large candidate pool: queries Open dictionary: all terms are feasible Efficiency: happens before search is executed User behavior: query formulation is different from typical writing Devices: different device may cause different types of typos Under-correction: even a term is in correct form, it may need correction Over-correction: a term that doesn’t appear correct could still be good search term Languages: Different languages have different challenges. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 13 / 31
- 14. Query Spelling Challenges Special Challenges (and Opportunities) in e-Commerce optimization target: linguistic correct or conversion? unique dictionary: model numbers, etc. high cost for over-correction availability of inventory data availability of conversion data Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 14 / 31
- 15. General problems Error modeling Candidate generation Ranking and selection of the best candidate. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 15 / 31
- 16. Modeling A Noisy Channel Framework Given user input query q, for every candidate correction c, compute the conditional probability p(c|q) p(c|q) = p(q|c) · p(c) p(q) ∝ p(q|c) · p(c) (1) Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 16 / 31
- 17. Modeling A Noisy Channel Framework (cont.) Source model p(c) Captures: how likely user will pick query c in the first place Typically: language model Rationale: common phrases have high probabilities Error model p(q|c) Captures: how likely c is misspelled as q Straightforward model: edit distance Rationale: misspelled query should not be too different from original query Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 17 / 31
- 18. Modeling A Noisy Channel Framework (cont.) More on Source model p(c) Linguistic correction is important Should also reflect query popularity In e-Commerce, we also need to consider query conversion, and query revenue Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 18 / 31
- 19. Modeling A Noisy Channel Framework (cont.) Language Model n-gram language model: data sparsity as n goes up backoff to/interpolation with lower-gram is necessary smoothing is important Good Turing smoothing: use 1-frequency items to estimate 0-frequency probabilities Additive smoothing: add pseudo count to terms/phrases Knesser-Ney Smoothing: smart way of backoff and interpolation Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 19 / 31
- 20. Modeling A Noisy Channel Framework (cont.) More on Error model p(q|c) Weighted edit model is better: p( a → e ) > p( a → n ) Context matters: p( a → e |context = ”be...”) Multi-word errors need to be considered: p(”gopro”|”go pro”), can be modeled by HMM, joint sequence model, etc. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 20 / 31
- 21. Modeling A Noisy Channel Framework (cont.) Hierarchical Error models Character level error model p( a → e |context = ”be...”) generalizes well less accurate Syllable level error model Word level error model p( pi → pie |context = ”life of ...”) sparse data more accurate Phrase level error model ... Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 21 / 31
- 22. Modeling Discriminative Models Why? Noisy channel model is a generative framework Multiplication is difficult as probabilities are estimated in different ways How to merge signals in one probability estimation is unknown (e.g. linguistic correction vs. popularity vs. revenue) There are other heuristic features and domain specific features that cannot be subsumed in noisy channel model Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 22 / 31
- 23. Modeling Discriminative Models (cont.) How? Learn to score < q, c > pair so that best correction has highest score Challenges Obtaining large scale training data: text parsing, human annotation Learning methods Classification Learning to Rank Structural learning Efficiency: use noisy channel model to retrieve a handful candidates Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 23 / 31
- 24. Modeling Discriminative Models (cont.) Typically discriminative models such as SVM can also be used to rerank the spelling candidates. Recent successes with deep neural net. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 24 / 31
- 25. Modeling Systems for Spelling Correction Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 25 / 31
- 26. Modeling Candidate generation for Spelling Correction Given a word find out all neighboring words under k edit distance. Given a word find out potential close matches by hashing trick. Generate candidates by using heuristic rules for common errors. N-gram based techniques. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 26 / 31
- 27. Modeling Candidate generation scaling up Distributed implementation. Hashing tricks. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 27 / 31
- 28. Modeling Spell correction for E-commerce UI for the spell correction. Input data: Whether to include item titles or not? Impact of autocorrection on conversion. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 28 / 31
- 29. Modeling References I Michele Banko and Eric Brill, Scaling to very very large corpora for natural language disambiguation, Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, 2001, pp. 26–33. Silviu Cucerzan and Eric Brill, Spelling correction as an iterative process that exploits the collective knowledge of web users., EMNLP, vol. 4, 2004, pp. 293–300. Huizhong Duan and Bo-June Paul Hsu, Online spelling correction for query completion, Proceedings of the 20th international conference on World wide web, ACM, 2011, pp. 117–126. Daniel Jurafsky and James H Martin, Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 29 / 31
- 30. Modeling References II Mark D Kernighan, Kenneth W Church, and William A Gale, A spelling correction program based on a noisy channel model, Proceedings of the 13th conference on Computational linguistics-Volume 2, Association for Computational Linguistics, 1990, pp. 205–210. Karen Kukich, Techniques for automatically correcting words in text, ACM Computing Surveys (CSUR) 24 (1992), no. 4, 377–439. Yanen Li, Huizhong Duan, and ChengXiang Zhai, A generalized hidden markov model with discriminative training for query spelling correction, Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, ACM, 2012, pp. 611–620. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 30 / 31
- 31. Modeling References III James L Peterson, Computer programs for detecting and correcting spelling errors, Communications of the ACM 23 (1980), no. 12, 676–687. Anjan Goswami, HuiZhong Duan (Search Science, WalmartLabs)Spell correction Systems 31 / 31