1. Improving Automated Patent Claim Parsing：
SINICA IIS 2017, Summer Intern
Pei Shuang, Haung
9, Aug, 2017
Improving Automated Patent Claim Parsing：
Dataset, System, and Experiment
Mengke Hu, David Cinciruk, John MacLaren Walsh
Subject : arXiv: [cs.CL]
Submission : May 2016

2. OUTLINE 01 05 02 04 03 Introduction Application
Experimental Validation: Improved Patent Subject Classification
Collect Curated Tag Corpus 02
Dataset Collection via Amazon
Mechanical Turk
Improve Claim NLP 04
Verb Corrector 03
Experimental Validation : Improved Claim NLP
Automatic Parts of Speech Tag Fixer

3. Introduction 01

4. Why is Patent ? What is Patent Claim? 01 Patent Trend Analysis
INTRODUCTION
Why is Patent ?
What is Patent Claim?
Trend Analysis
Technology Forecasting
Competitor Analysis
Infringement Analysis …
The key of one patent
申請專利範圍
補充點先備知識

5. “ ” One Sentence - Describe this paper01
Briefly
INTRODUCTION
One Sentence - Describe this paper “
Stanford NLP Parsing software not work well on Patent Claim,
so, therefore, Authors design Corrector to check and build real Parsing Tree ”
Why : 1. 句法過長 2. 有些常用詞，詞性不一定對
1. To begin with, patent claims involve sentences that are much longer than those typically found in other sources of text.
2. Another source of mismatch, which remains after chunking, arises from the use of words as less common parts of speech in patent claim language. For instance “said” most commonly functions as an adjective, and “claim” typically functions as a noun, when occurring in patent claims,
花錢找人重新全部標記太放費錢

6. 02 Collect Curated Tag Corpus
Dataset Collection via Amazon Mechanical Turk

7. AMT (Amazon Mechanical Turk )02
Where
Collect Curated Tag Corpus
AMT (Amazon Mechanical Turk )
A platform, where you can post TASK
H I T (Human Intelligent Task)
Worker get paid when finishing the task
Curated Tag
補充點先備知識
Each HIT = 18 real + 2 test
If accuracy > 0.9 :
2 test are curated Tag
Else :
uncurated Tag

8. 02 Change 1 Campaign 1 2 Campaign 2 3 Campaign 3 4 Campaign 4 18 days
Collect Curated Tag Corpus
Campaign 1 1
18 days
29 days
Campaign 2 2 3
32 days
Campaign 3
6 days 4
Campaign 4

9. “ ” Only 69% After collecting 174246 curated tags and02
Result
Collect Curated Tag Corpus
After collecting curated tags and
identifying these as benchmark, we find that… “
The accuracy of Stanford NLP Parsing software on verb is
Why : 1. 句法過長 2. 有些常用詞，詞性不一定對
1. To begin with, patent claims involve sentences that are much longer than those typically found in other sources of text.
2. Another source of mismatch, which remains after chunking, arises from the use of words as less common parts of speech in patent claim language. For instance “said” most commonly functions as an adjective, and “claim” typically functions as a noun, when occurring in patent claims,
Only 69% ”

10. Verb Corrector 03
Automatic Parts of Speech Tag Fixer

11. What is `Verb Corrector’?03
Function
Verb Corrector
What is `Verb Corrector’?
A classifier trained by SVM, output N, V, adj …
Why `Verb’ ?
Right verb-tag, almost right parsing tree
How to train ?
concatenated
Using curated AMT tag corpus,
only train `Verb Triplicate’
by just correcting the incorrect verbs tags and rerunning the parser, a correct parsing can often be obtained
所以第二章節所提到的在AMT上每一個Human Intelligent Task 每個任務中的20題，幾乎都focus 在那些看起來是動詞的詞

12. After training, how it work ?03
Flowchart
Verb Corrector
Word2Vec (s)
After training, how it work ?
Step4
Verb Triplicate(s)
Step3
SVM
Rule-Based Changing
Step5
Step2
Stanford Parsing
Step1
Step6
Step7
Input `Segment’
Parsing Tree (s)
Parsing Tree
(Highest score)

13. Improve Claim NLP 04
Experimental Validation : Improved Claim NLP

14. Percentage of Error about verb-tag ?04
Error 1
Improve Claim NLP
Percentage of Error about verb-tag ? 0%
10%
20%
30%
40%
Stanford POS Tagging
Rule-Based Corrector
; ; 9.17
Verb Corrector
Rule-Based + SVM

15. Comparing two parsing tree04
Error 2
Improve Claim NLP
Comparing two parsing tree
ground truth trees can be created by taking the portion of the AMT corpus that was not utilized in training the tag corrector,
Calculate Precision & Recall

16. Comparing Stanford Parsing and Verb Corrector04
Error 2
Improve Claim NLP
Comparing Stanford Parsing and Verb Corrector
左邊是都和標準答案比
Corpus 分成兩半 -> 一半訓練SVM, 一半拿來當正確答案 和丟入SVM
右邊是以Verb Corrector 當正確答案
看得出Verb Corrector 更接近正確答案

17. Application 05
Experimental Validation: Improved Patent Subject Classification

18. Why Patent Classification ?05
Function
Application
Why Patent Classification ?
Patent Search, avoiding Infringement
Big topic !

19. Conclusion and What we can learn from06

20. 1：Experience 2：Domain Knowledge 05 总结回顾
REVIEW
1：Experience
How to handle similar domain problem like Patent Claim
2：Domain Knowledge
(1) paper proposed an alternative way of adapting NLP software to claim language by simply correcting the POS tags of putative verbs
(2) Rule- Based Corrector
(3) Verb Triplicate (?)

21. Speaker：Pei Shuang, Haung
Thank you
Speaker：Pei Shuang, Haung