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専門用語抽出手法の研究と 抽出アプリケーションの開発
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Koga Kobayashi
September 27, 2018
Programming
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専門用語抽出手法の研究と 抽出アプリケーションの開発
Koga Kobayashi
September 27, 2018
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Transcript
ઐ༻ޠநग़ख๏ͷݚڀͱ நग़ΞϓϦέʔγϣϯͷ։ൃ
ࣗݾհ • খྛᕣՏ: @kajyuuen • ஜେֶ ใֶ܈ 4 • ݚڀࣗવݴޠॲཧɺػցֶश
• ։ൃͰRuby on RailsΛΑ͍͘·͢ • झຯ • ΠϯλʔωοτɺԻָؑɺόΠΫ(͓ٳΈத) 2
త 3 ڭࢣσʔλ͕গͳ͍ઐυϝΠϯͷจষ͔Β ઐ༻ޠΛநग़͕ग़དྷΔγεςϜɾख๏ͷ։ൃ
ઐ༻ޠͱ ઐ༻ޠʢͤΜΜΑ͏͝ʣͱɺ͋Δಛఆͷ৬ۀʹैࣄ͢Δऀɺ ͋ΔಛఆͷֶͷɺۀքͷؒͰͷΈ༻͞Εɺ௨༻͢Δݴ༿ɾ༻ޠ܈Ͱ͋Δɻ ςΫχΧϧλʔϜʢӳޠ technical termʣͱݴΘΕΔɻ Wikipedia͔ΒͷҾ༻ 4 ྫ: ίʔϧηϯλʔ
• ΦϖϨʔλʔɺFAQɺVoCɺฏۉ௨࣌ؒ ྫ: ྉཧ • ͍ͪΐ͏Γɺܡണ͖ɺࡾຕ͓Ζ͠
എܠ ͔͠͠… • ҰൠͷυϝΠϯͰֶशͨ͠ϞσϧΛ ઐυϝΠϯʹదԠ্ͤͯ͞ख͘நग़ग़དྷͳ͍ • ઐ༻ޠͷநग़ʹઐՈͷଟ͘ͷ࣌ؒͱਓख͕ඞཁ ͱ͍͏͕͋Γɺઐ༻ޠͷநग़͔ͬͨ͠ 5 ઐ༻ޠͷࣙॻܗଶૉղੳݕࡧͷਫ਼Λ্ͤ͞Δ
എܠ ͦͷͨΊগͳ͍ίετͰઐ༻ޠநग़͕ՄೳʹͳΔ͜ͱ ϨτϦόͷੑೳ্ʹܨ͕Δ 6
ఏҊख๏ • ग़ݱසͱ࿈සʹΑΔઐ༻ޠͷީิநग़ • ೳಈֶशΛ༻͍ͨڭࢣ͋ΓֶशʹΑΔઐ༻ޠީิͷྨ 7 ͜ΕΒ2ͭͷख๏ΛΈ߹ΘͤΔ͜ͱͰ ίετͰͷઐ༻ޠநग़ΛՄೳʹ͢Δ
ઐ༻ޠநग़·Ͱͷϑϩʔ 8 ग़ݱසͱ࿈සʹΑΔઐ༻ޠީิநग़ ೳಈֶशΛ༻͍ͨڭࢣ͋Γֶश ઐ༻ޠͷநग़
ग़ݱසͱ࿈සʹΑΔઐ༻ޠީิநग़[த+ 2003] • ઐ༻ޠ໊ࢺͦͷͷ͔ෳͷ໊ࢺͷෳ߹ޠ͔ΒΔͱԾఆ • ෳ߹ޠΛߏ͢Δ࠷খ୯ҐΛ୯໊ࢺͱఆٛ • ͋Δ୯໊ࢺ͕ଞͷ୯໊ࢺͱ࿈݁ͯ͠ ෳ߹ޠΛ࡞Δճ͕ଟ͍΄Ͳॏཁ㱺ઐ༻ޠ 9
ࣗવݴޠॲཧ ࣗવ ݴޠ ॲཧ = + +
ग़ݱසͱ࿈සʹΑΔઐ༻ޠީิநग़[த+ 2003] ྫ: ࣗવݴޠॲཧ 10 ୯໊ࢺ લͷޠʹ࿈݁ͨ͠ճ ޙͷޠʹ࿈݁ͨ͠ճ ࣗવ
ݴޠ ॲཧ ॏཁ = ෳ߹ޠΛ࡞Δ୯໊ࢺͷ࿈݁ճͷ૬ฏۉ = 6 1 ⋅ 2 ⋅ 2 ⋅ 3 ⋅ 1 ⋅ 1 = 1.51
ઐ༻ޠநग़·Ͱͷϑϩʔ 11 ग़ݱසͱ࿈සʹΑΔઐ༻ޠީิநग़ ೳಈֶशΛ༻͍ͨڭࢣ͋Γֶश ઐ༻ޠͷநग़
ೳಈֶशͱ ࢁͷϥϕϧͳ͠σʔλͷத͔Β ϥϕϧ͕͘ͱϞσϧͷੑೳ্͕ͦ͠͏ͳσʔλΛϢʔβʹਪન͠ Ξϊςʔγϣϯ͍ͯ͘͜͠ͱͰϞσϧΛֶश͍ͯ͘͠ํ๏ 12 গͳ͍ڭࢣσʔλͰϞσϧͷੑೳ্͕͢Δ
ೳಈֶशͱ 13 ઐ༻ޠ ඇઐ༻ޠ ϥϕϧͳ͠ 1 2 ϥϕϧ͕Γ͍ͨσʔλ?
ೳಈֶशͱ 14 ઐ༻ޠ ඇઐ༻ޠ ϥϕϧͳ͠ 1 2 ϥϕϧ͕Γ͍ͨσʔλ? ޮՌతͳֶश͕ߦ͑ͳ͍
ೳಈֶशͱ 15 ઐ༻ޠ ඇઐ༻ޠ ϥϕϧͳ͠ 1 2 ϥϕϧ͕Γ͍ͨσʔλ? ֶश͕ޮՌతʹਐΉ
ಛྔϕΫτϧͷ࡞ • લޙೋ୯ޠͷදܥͱࢺͱจࣈछ • ڭࢣͳֶ͠शʹΑΔॏཁ ͔ΒಛྔϕΫτϧΛ࡞͢Δ 16 ݚڀ ࣗવݴޠॲཧ
ͱ ػց ֶश Ͱ͢ ໊ ॿ ઐ༻ޠީิ ॿ ໊ ໊ ॿಈ ݚڀ ࣗવݴޠॲཧ ͱ ػց ֶश Ͱ͢ 1.51 ݚڀ ࣗવݴޠॲཧ ͱ ػց ֶश Ͱ͢
Ϟσϧͷֶश Logistic regression • ͦͷ୯ޠ͕ઐ༻ޠ͔ඇઐ༻ޠ͔Λྨ͢ΔϞσϧ • ೳಈֶशͰֶशͱ༧ଌΛ܁Γฦ͢ҝ୯७ͳϞσϧΛ࠾༻ • ࠓճ༻͍Δೳಈֶशͷख๏Ͱ༧ଌ͕֬ඞཁ 17
σʔλબͱϞσϧͷߋ৽ Uncertainly Sampling (least confident) ݱ࣌ͷϞσϧͰ࠷ෆ͔֬ͳσʔλΛਪન 18 x* LC =
arg max x∈U 1 − Pθ ( ̂ y|x) ̂ y: ࠷औΓ͏Δ͕֬ߴ͍ϥϕϧ U : ϥϕϧͳ͠σʔλͷू߹ x* LC : ϥϕϧ͚Λਪન͢Δσʔλ
࣮ݧᶃ: Wikipediaʹରͯ͠ઐ༻ޠநग़ • σʔλ • Wikipediaͷจষ61ͭʹରͯ͠ઐ༻ޠͷநग़Λߦ͏ • ݅ઃఆ • ڭࢣͳֶ͠शͰநग़ͨ͠༻ޠͷࡾͷҰʹΞϊςʔγϣϯ
• 5ͭͷσʔλʹϥϕϦϯά͕ऴΘͬͨΒϞσϧΛ࠶ֶश • ೳಈֶशͱϥϯμϜαϯϓϦϯάɺࣙॻʹΑΔൺֱΛߦ͏ 19 ೳಈֶश͕ϥϯμϜαϯϓϦϯάΑΓ༏Ε͍ͯΔ͜ͱΛࣔ͢
࣮ݧᶃ: ݁Ռ IPAdic NEologd 20 Ϟσϧ 1SFDJTJPO 3FDBMM 'WBMVF ڭࢣͳֶ͠श
ϥϯμϜαϯϓϦϯά ೳಈֶश Ϟσϧ 1SFDJTJPO 3FDBMM 'WBMVF ڭࢣͳֶ͠श ϥϯμϜαϯϓϦϯά ೳಈֶश • ྆ࣙॻʹ͓͍ͯϥϯμϜαϯϓϦϯάΑΓೳಈֶश͕༏Ε͍ͯͨ • NEologdΛ༻ͨ͠΄͏͕ੑೳ͕ߴ͔ͬͨ
࣮ݧᶄ: FAQυϝΠϯʹରͯ͠ͷઐ༻ޠநग़ • ֶशσʔλ • εΧύʔʂͷϔϧϓίϯςϯπ͔Βऔಘͨ͠FAQ 5,113จࣈ • ݅ઃఆ •
ϥϯμϜʹΞϊςʔγϣϯ͢ΔϞσϧͱൺֱ • 5ͭͷσʔλʹϥϕϦϯά͕ऴΘͬͨΒϞσϧΛ࠶ֶश • Ξϊςʔγϣϯ͕0ͷͱ͖શͯͷநग़୯ޠΛઐ༻ޠͱΈͳ͢ 21 ͲͷఔΞϊςʔγϣϯ͢Ε࣮༻తͳϞσϧʹͳΔ͔֬ೝ IUUQTIFMQDFOUFSTLZQFSGFDUWDPKQ
࣮ݧᶄ: ਫ਼ͱ࠶ݱ 22 • ਫ਼ೳಈֶश͕ϥϯμϜαϯϓϦϯάΑΓઌʹανΔ • ࠶ݱͰೳಈֶशϥϯμϜαϯϓϦϯάΛେ্͖͘ճΔ Ξϊςʔγϣϯͳͩ͠ͱ ਫ਼͍ ڭࢣͳֶ͠श
ઐ༻ޠͷ72.7%ΛΧόʔ ڭࢣͳֶ͠श
࣮ݧᶄ: F 23 ׂ࢛ఔΞϊςʔγϣϯΛߦ͏͚ͩͰF7ׂΛ͑ͨ ࠷େͰ20ϙΠϯτͷࠩ
நग़ʹޭͨ͠ઐ༻ޠ • εΧύʔʂɺϓϨϛΞϜαʔϏεޫϚϯγϣϯ͚αʔϏε நग़ग़དྷͳ͔ͬͨઐ༻ޠ • TZ-WR4KPɺSP-HR200HɺΞϯςφαϙʔτϓϥϯ ؒҧͬͯநग़ͯ͠͠·ͬͨ୯ޠ • ൪ɺνϟϯωϧɺMyνϟϯωϧ1 ࣮ݧᶄ:
ڭࢣͳֶ͠शͰͷநग़୯ޠྫ 24
ΠϯλʔϑΣʔε ΞϊςʔγϣϯͷޮΛ্͛ΔͨΊʹ WebΞϓϦέʔγϣϯͱͯ͠ΠϯλʔϑΣʔεΛ։ൃͨ͠ 25 ػೳҰཡ • ઐ༻ޠͷϋΠϥΠτ / நग़ػೳ •
ೳಈֶशʹΑΔֶशͱΞϊςʔγϣϯσʔλͷਪન • CSVΤΫεϙʔτ
DEMO 26
ΞϓϦέʔγϣϯͷߏ 27
·ͱΊ 28 త গͳ͍ςΩετσʔλ͔Βઐ༻ޠͷநग़Λߦ͏ ख๏ ڭࢣͳֶ͠श+ೳಈֶशΛ༻͍ͨWebΞϓϦέʔγϣϯͷఏڙ ࠓޙ நग़ΞϧΰϦζϜͷ࠶࣮ʹΑΔߴԽ ݕࡧͳͲͷԠ༻ʹ͓͚ΔੑೳධՁɺ৽ͨͳख๏ɾಛྔͷௐࠪ
ࢀߟจݙ [1] த ༟ࢤ, ౬ຊ ߛজ, ୢଇ. ग़ݱසͱ࿈සʹجͮ͘ઐ༻ޠநग़. ࣗવݴޠॲཧ.
2003, 10(1), p.27-45. [2] த ༟ࢤ, ౬ຊ ߛজ, ୢଇ. ຊޠϚχϡΞϧจʹ͓͚Δ໊ࢺؒͷ࿈ใΛ༻͍ͨϋΠύʔςΩε τԽͷͨΊͷࡧҾޠͷநग़. ใॲཧֶձݚڀใࠂࣗવݴޠॲཧ. 1996, (114), p.65-72 [3] “ઐ༻ޠʢΩʔϫʔυʣࣗಈநग़༻PerlϞδϡʔϧ ”. ”ઐ༻ޠʢΩʔϫʔυʣࣗಈநग़γεςϜ”ͷ ϖʔδΑ͏ͦ͜. http://gensen.dl.itc.u-tokyo.ac.jp/termextract.html, (ࢀর 2018-9-4). [4] Burr Settles. Active Learning Literature Survey. Computer Sciences Technical Report 1648. 2010. http://burrsettles.com/pub/settles.activelearning.pdf, (ࢀর 2018-9-4). [5] Burr Settles, Mark Craven. An Analysis of Active Learning Strategies for Sequence Labeling Tasks. EMNLP. 2008. 29