An Introduction to NLP4L - Natural Language Processing Tool for Apache Lucene: Presented by Tomoko Uchida, Rondhuit Co. Ltd.
•
3 gostaram•1,578 visualizações
Lucene/Solr Revolution 2015
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Semelhante a An Introduction to NLP4L - Natural Language Processing Tool for Apache Lucene: Presented by Tomoko Uchida, Rondhuit Co. Ltd.
Semelhante a An Introduction to NLP4L - Natural Language Processing Tool for Apache Lucene: Presented by Tomoko Uchida, Rondhuit Co. Ltd. (20)
Mais de Lucidworks
Mais de Lucidworks (20)
Último
Último (20)
An Introduction to NLP4L - Natural Language Processing Tool for Apache Lucene: Presented by Tomoko Uchida, Rondhuit Co. Ltd.
- 1. O C T O B E R 1 3 - 1 6 , 2 0 1 6 • A U S T I N , T X
- 2. An Introduction to NLP4L: Natural Language Processing Tool for Apache Lucene Tomoko Uchida Consultant, Rondhuit Co. Ltd.
- 3. 3 Who am I • Tomoko Uchida (@moco_beta) • Luke (Lucene Toolbox) collaborator (2015 ~) • https://github.com/DmitryKey/luke • The best-known tool for debugging and learning Lucene/Solr, Elasticsearch index :-)
- 4. 4 Agenda • What’s NLP4L? • How NLP improves search experience • Calculate probabilities using ShingleFilter • Transliteration (Application for HMM) • NLP4L Framework (coming soon)
- 5. 5 Agenda • What’s NLP4L? • How NLP improves search experience • Calculate probabilities using ShingleFilter • Transliteration (Application for HMM) • NLP4L Framework (coming soon)
- 6. 6 What’s NLP4L? • GOAL • Improve Lucene users’ search experience • FEATURES • Use of Lucene index as a Corpus Database • Lucene API Front-end written in Scala • NLP4L provides • Preprocessors for existing ML tools • Provision of ML algorithms and Apprications (e.g. Transliteration)
- 7. 7 What’s NLP4L? • GOAL • Improve Lucene users’ search experience • FEATURES • Use of Lucene index as a Corpus Database • Lucene API Front-end written in Scala • NLP4L provides • Preprocessors for existing ML tools • Provision of ML algorithms and Apprications (e.g. Transliteration)
- 8. 8 What’s NLP4L? • GOAL • Improve Lucene users’ search experience • FEATURES • Use of Lucene index as a Corpus Database • Lucene API Front-end written in Scala • NLP4L provides • Preprocessors for existing ML tools • Provision of ML algorithms and Applications (e.g. Transliteration)
- 9. 9 Agenda • What’s NLP4L? • How NLP improves search experience • Calculate probabilities using ShingleFilter • Transliteration (Application for HMM) • NLP4L Framework (coming soon)
- 10. targetresult tpfp fn tn precision = tp / (tp + fp) recall = tp / (tp + fn) 10 Evaluation Measures
- 11. targetresult tpfp fn tn precision = tp / (tp + fp) recall = tp / (tp + fn) 11 Evaluation Measures
- 12. targetresult tpfp fn tn precision = tp / (tp + fp) recall = tp / (tp + fn) 12 Evaluation Measures
- 13. targetresult tpfp fn tn precision = tp / (tp + fp) recall = tp / (tp + fn) 13 Evaluation Measures
- 14. Recall ,Precision tpfp fn tn precision = tp / (tp + fp) recall = tp / (tp + fn) 14
- 15. Recall ,Precision targetresult tpfp fn tn precision = tp / (tp + fp) recall = tp / (tp + fn) 15
- 16. n-gram, synonym dictionary, etc. facet (filter query) Ranking Tuning recall precision recall , precision 16 Solution
- 17. n-gram, synonym dictionary, etc. facet (filter query) Ranking Tuning recall precision recall , precision 17 Solution
- 18. n-gram, synonym dictionary, etc. e.g. Transliteration facet (filter query) recall precision recall , precision Ranking Tuning 18 Solution
- 19. n-gram, synonym dictionary, etc. e.g. Transliteration facet (filter query) e.g. Named Entity Extraction recall precision recall , precision Ranking Tuning 19 Solution
- 21. filter by Gender=Men s 21 targetresult gradual precision improvement
- 22. 22 targetresult filter by Gender=Men s filter by Price=100-150 gradual precision improvement
- 23. ID product price gender 1 CURREN New Men s Date Stainless Steel Military Sport Quartz Wrist Watch 8.92 Men s 2 Suiksilver The Gamer Watch 87.99 Men s 23 Structured Documents
- 24. ID article 1 David Cameron says he has a mandate to pursue EU reform following the Conservatives' general election victory. The Prime Minister will be hoping his majority government will give him extra leverage in Brussels. 2 He wants to renegotiate the terms of the UK's membership ahead of a referendum by the end of 2017. He has said he will campaign for Britain to remain in the EU if he gets the reforms he wants. 24 Unstructured Documents
- 25. I D article person org loc 1 David Cameron says he has a mandate to pursue EU reform following the Conservatives' general election victory. The Prime Minister will be hoping his majority government will give him extra leverage in Brussels. David Cameron EU Brussels 2 He wants to renegotiate the terms of the UK's membership ahead of a referendum by the end of 2017. He has said he will campaign for Britain to remain in the EU if he gets the reforms he wants. EU UK Britain NEE[1] extracts interesting words. [1] Named Entity Extraction 25 Make them Structured
- 26. 26 Agenda • What’s NLP4L? • How NLP improves search experience • Calculate probabilities using ShingleFilter • Transliteration (Application for HMM) • NLP4L Framework (coming soon)
- 27. 27 Language Model • LM represents the fluency of language
- 28. 28 Language Model • LM represents the fluency of language
- 29. • LM represents the fluency of language • N-gram model is the LM which is most widely used 29 Language Model
- 30. • LM represents the fluency of language • N-gram model is the LM which is most widely used • Calculation example for 2-gram 30 totalTermFreq(”word2g”,”an apple”) totalTermFreq(”word”,”an”) Language Model
- 31. Alice/NNP ate/VB an/AT apple/NNP ./. Mike/NNP likes/VB an/AT orange/NNP ./. An/AT apple/NNP is/VB red/JJ ./. NNP Proper noun, singular VB Verb AT Article JJ Adjective . period 31 Our Corpus for training Part-of-Speech Tagging
- 33. 33 Series of Words Hidden Markov Model
- 34. 34 Series of Part-of-Speech Hidden Markov Model
- 37. NNP 0.667 VB 0.0 . 0.0 JJ 0.0 AT 0.333 1.0 1.0 0.4 0.6 0.6670.333 37 alice 0.2 apple 0.4 mike 0.2 orange 0.2 ate 0.333 is 0.333 likes 0.333 an 1.0 red 1.0 . 1.0 HMM state diagram
- 38. 38 Agenda • What’s NLP4L? • How NLP improves search experience • Calculate probabilities using ShingleFilter • Transliteration (Application for HMM) • NLP4L Framework (coming soon)
- 39. 39 Transliteration Transliteration is a process of transcribing letters or words from one alphabet to another one to facilitate comprehension and pronunciation for non-native speakers.
- 40. computer コンピューター server サーバー internet インターネット mouse マウス information インフォメーション examples of transliteration from English to Japanese 40 Transliteration
- 41. you search English mouse 41 It helps improve recall
- 42. but you got マウス (=mouse) highlighted in Japanese 42 It helps improve recall
- 45. nlp4l> :load examples/trans_katakana_alpha.scala 45 Demo: Transliteration val indexer = new HmmModelIndexer(index) val file = Source.fromFile("train_data/alpha_katakana_aligned.txt", "UTF-8") val pattern: Regex = """([u30A0-u30FF]+)([a-zA-Z]+)(.*)""".r def align(result: List[(String, String)], str: String): List[(String, String)] = { str match { case pattern(a, b, c) => align(result :+ (a, b), c) case _ => result } } // create hmm model index file.getLines.foreach{ line: String => val doc = align(List.empty[(String, String)], line) indexer.addDocument(doc) }
- 46. Input Prediction Right Answer アルゴリズム algorism algorithm プログラム program (OK) ケミカル chaemmical chemical ダイニング dining (OK) コミッター committer (OK) エントリー entree entry 46 Demo: Transliteration
- 47. ① crawl gathering Katakana-Alphabet string pairs アルゴリズム, algorithm Transliteration アルゴリズム algorism calculate edit distance synonyms.txt 47 store pair of strings if edit distance is small enough ② ③ ④ ⑤ ⑥ Gathering loan words
- 48. 48 Agenda • What’s NLP4L? • How NLP improves search experience • Calculate probabilities using ShingleFilter • Transliteration (Application for HMM) • NLP4L Framework (coming soon)
- 49. 49 NLP4L Framework • A framework that improves search experience (for mainly Lucene- based search system). Pluggable. • Reference implementation of plug-ins and corpora provided. • Uses NLP/ML technologies to output models, dictionaries and indexes. • Since NLP/ML are not perfect, an interface that enables users to personally examine output dictionaries is provided as well.
- 50. 50 NLP4L Framework • A framework that improves search experience (for mainly Lucene- based search system). Pluggable. • Reference implementation of plug-ins and corpora provided. • Uses NLP/ML technologies to output models, dictionaries and indexes. • Since NLP/ML are not perfect, an interface that enables users to personally examine output dictionaries is provided as well.
- 51. 51 NLP4L Framework • A framework that improves search experience (for mainly Lucene- based search system). Pluggable. • Reference implementation of plug-ins and corpora provided. • Uses NLP/ML technologies to output models, dictionaries and indexes. • Since NLP/ML are not perfect, an interface that enables users to personally examine output dictionaries is provided as well.
- 52. 52 NLP4L Framework • A framework that improves search experience (for mainly Lucene- based search system). Pluggable. • Reference implementation of plug-ins and corpora provided. • Uses NLP/ML technologies to output models, dictionaries and indexes. • Since NLP/ML are not perfect, an interface that enables users to personally examine output dictionaries is provided as well.
- 53. 53 Solr ES Mahout Spark Data Source ・Corpus (Text data, Lucene index) ・Query Log ・Access Log Dictionaries ・Suggestion (auto complete) ・Did you mean? ・synonyms.txt ・userdic.txt ・keyword attachment maintenance Model files Tagged Corpus Document Vectors ・TermExtractor ・Transliteration ・NEE ・Classification ・Document Vectors ・Language Detection ・Learning to Rank ・Personalized Search
- 54. 54 Solr ES Mahout Spark Data Source ・Corpus (Text data, Lucene index) ・Query Log ・Access Log Dictionaries ・Suggestion (auto complete) ・Did you mean? ・synonyms.txt ・userdic.txt ・keyword attachment maintenance Model files Tagged Corpus Document Vectors ・TermExtractor ・Transliteration ・NEE ・Classification ・Document Vectors ・Language Detection ・Learning to Rank ・Personalized Search
- 55. 55 Solr ES Data Source ・Corpus (Text data, Lucene index) ・Query Log ・Access Log Dictionaries ・Suggestion (auto complete) ・Did you mean? ・synonyms.txt ・userdic.txt ・keyword attachment maintenance ・TermExtractor ・Transliteration ・NEE ・Classification ・Document Vectors ・Language Detection ・Learning to Rank ・Personalized Search Mahout Spark Model files Document Vectors Tagged Corpus
- 56. 56 Mahout Spark Data Source ・Corpus (Text data, Lucene index) ・Query Log ・Access Log Dictionaries ・Suggestion (auto complete) ・Did you mean? ・synonyms.txt ・userdic.txt ・keyword attachment maintenance Model files Tagged Corpus Document Vectors ・TermExtractor ・Transliteration ・NEE ・Classification ・Document Vectors ・Language Detection ・Learning to Rank ・Personalized Search Solr ES
- 57. 57 example: Keyword Attachment Information about associated Solr collection (core) NLP/ML task (processor) chain described by HOCON (Human- Optimized Config Object Notation) UI prototype for NLP4L Framework (Lucia) https://github.com/NLP4L/lucia
- 58. 58 example: Keyword Attachment Extracted keywords from whole documents ex.) Named Entities by OpenNLP
- 59. 59 example: Keyword Attachment Information about associated Solr document (unique key, etc.) Extracted keywords from this document Solr field name for each keyword
- 60. 60 example: Keyword Attachment Check the keywords and remove wrong / inappropriate entries
- 61. 61 example: Keyword Attachment Synch (attach) all keywords to Solr documents (by partial update command)
- 62. 62 example: Keyword Attachment Solr document (befere keywords are attached)
- 63. 63 example: Keyword Attachment Solr document (after keywords are attached)
- 64. 64 example: Keyword Attachment If you delete keyword(s) already have been attached to solr documents, the keyword(s) also will be removed from solr index when next “synch” action executed.
- 65. 65 Lucene doc Lucene doc keyword ↑ Increase boost Keyword Attachment Application • “Keyword attachment” is a general format that enables the following functions. • Learning to Rank • Personalized Search • Named Entity Extraction • Document Classification
- 66. 66 targetresult 1 2 3 … 50 100 500 … Before Learning to Rank
- 67. 67 targetresult 1 2 3 … 50 100 500 … After Learning to Rank
- 68. • Program learns, from access log and other sources, that the score of document d for a query q should be larger than the normal score(q,d) 68 Lucene doc d q, q, … https://en.wikipedia.org/wiki/Learning_to_rank Learning to Rank
- 69. 69 targetresult 1 2 3 … 50 100 500 … q=apple computer … Personalized Search
- 70. 70 target result 50 100 500 … 1 2 3 … q=apple fruit … Personalized Search
- 71. 71 Lucene doc d1 q1u1, q2u2 Lucene doc d2 q2u1, q1u2 Personalized Search • Program learns, from access log and other sources, that the score of document d for a query q by user u should be larger than the normal score(q,d) • Since you cannot specify score(q,d,u) as Lucene restricts doing so, you have to specify score(qu,d). • Limit the data to high-order queries or divide fields depending on a user as the number of q-u combinations can be enormous.
- 72. 72 example: Generating Synonyms (loanwords) Execute job that generate pairs of Katakana and corresponding English words from corpus
- 73. 73 example: Generating Synonyms (loanwords) Make adjustments in auto generated pairs (candidate synonyms) via web UI
- 74. 74 example: Generating Synonyms (loanwords) acacia,アカシア academy,アカデミー acatenango,アカテナンゴ access,アクセス accident,アクシデント action,アクション active,アクティブ activision,アクティビジョン acton,アクトン actor,アクター …… Exported pairs can be used in SynonymFilter synonyms_loadwords_ja.txt
- 75. 75 Contact us at koji at apache dot org for the details. Join and Code with Us! https://github.com/NLP4L
- 76. 76 Thank you!