FLOW: A First-Language-Oriented Writing Assistant System

1. FLOW: A First-Language-Oriented Writing Assistant System Mei-Hua Chen*, Shih-Ting Huang+, Hung-Ting Hsieh*, Ting-Hui Kao+, Jason S. Chang+ * Institute of Information Systems and Applications + Department of Computer Science National Tsing Hua University HsinChu, Taiwan, R.O.C. 30013 ACL 2012

2. Feature • First-Language-Oriented • Translations • Paraphrases • N-grams (N=5)

3. Introduction • composing stage We propose a method to ” 解決問題“. solve the problem tackle the problem • revising stage We propose a method to solve the problem 盡力 try our best do our best

4. Translation-based N-gram Prediction • {e1, e2, …em, f1, f2 …fn} • predict the possible translations (Och and Ney, 2003) bilingual phrase alignments 2. disambiguous (correct the alignment error) ex. ...on ways to identify tackle 洗錢 money laundering money His forum entitled money laundry

5. Paraphrase Suggestion • {e1, e2,…ek} • pivot-based method proposed by Bannard and Callison-Burch (2005).

6. Experiment • Training data: Hong Kong Parallel Text (2,220,570 Chinese-Englishsentence pairs) • 10 Chinese sentences • two students to translate the Chinese sentences to English sentences using FLOW

7. Result • Paraphrase performance well • N-gram tends to produce shorter • phrases

8. Exploration of Term Dependence in Sentence Retrieval Keke Cai, Jiajun Bu, Chun Chen, Kangmiao Liu College of Computer Science, Zhejiang University Hangzhou, 310027, China ACL 2007

9. Sentence Retrieval • Limited information • Application: • document summarization • question answering • novelty detection

10. Term Dependence • Query:{Everest, highest , mountain} • Q ={TS1, TS2, …, TSn} • Term combinations:{Everest highest, highest mountain, Everest mountain} • further evaluated in each retrieved sentence • Ex. Everest is the highest mountain

11. MINIPAR • a dependency parser • Ex. Everest is the highest mountain • :{Everest highest, highest mountain, Everest mountain} Distance=(3+1+2)/3

12. Association Strength : Size of D( ) :

13. Discussion • Query:{ Everest, highest , mountain} • TS1:{ Everest, highest , mountain} TS2:{ highest , mountain} AS(TS1, S1)= 0.5^(1/3)*0.5^2=0.1984 AS(TS2, S2)= 0.5^(1/2)*0.5^1=0.35355 • Dependency distance tend to small set pairs

14. Experiments • Testing data: TREC novelty track 2003 and 2004 • Average precision of each different retrievalmodels

16. Paraphrasing with Bilingual Parallel Corpora Colin Bannard , Chris Callison-Burch School of Informatics University of Edinburgh 2 Buccleuch Place Edinburgh, EH8 9LW ACL 2005

17. Parallel Corpora • Monolingual • Bilingual (German-English)

18. Bilingual Parallel Corpora • much more commonly available resource • one language can be identified using a phrase in another language as a pivot. German is pivot, use it to find English phrase

19. Paraphrases • Application multidocument summarization machine translation question answering

20. Aligning phrase pairs • statistical machine translation • phrase alignment • Och and Ney(2003) Giza++

21. Assigning probabilities : original English phrase : candidate English phrase : foreign language phrase

22. Experimental Design1 • 46 English phrases (occurred multiple times in the first 50,000 sentences) • Corpus: German-English section of the Europarl corpus (1,036,000 German-English sentence pairs) • Manually aligned • 289 evaluation sets (each contain 2~10) • Judgment: (meaning and grammar) two native English speakers • Precision: 0.605

25. Experimental Design2 • evaluated the accuracy of top ranked paraphrases • conditions 1. manual alignments 2. automatic alignments 3. automatic alignments & multiple corpora in different languages (French-English, Spanish-English, Italian-English) (4,000,000 sentence pairs) 4. re-ranking 5. limited to the same sense

26. trigram language model Ignore Grammar

28. Image Search by Concept Map Hao Xu† Jingdong Wang‡ Xian-Sheng Hua‡ Shipeng Li‡ †MOE-MS KeyLab of MCC, University of Science and Technology of China, Hefei, 230026, P. R. China ‡Microsoft Research Asia, Beijing 100190, P. R. China SIGIR 2010

30. Image search schemes

31. Flowchart

32. Visual Instance Transformation • text-based image search (Top 50) • affinity propagation (AP) clustering algorithm • sort the obtained centers in a descending order of their groups sizes

33. Visual Instance • snoopy Side view Front view

34. Spatial Intention Estimation • position • influence scope • Use 2D Gaussian distribution

35. Layout Sensitive Relevance Evaluation • Sum up the relevance score for each concept • Appearance consistency -the count of common visual words • Spatial consistency -desired spatial distribution of the concept k -spatial distribution of visual instance v in the image

36. Quantitative Search Performance

37. Visual Results (1)

38. Visual Results (2)

39. User Study • participants : 20 college students • To the question “have you ever had any image search intentionconcerning the concept layout?” • 20% of respondents replied with“yes” and 50% of respondents replied with “no, but probably in the future”.

40. Modeling Higher-Order Term Dependencies in Information Retrieval using Query Hypergraphs Michael Bendersky , W. Bruce Croft Dept. of Computer Science Univ. of Massachusetts Amherst Amherst, MA SIGIR 2012

41. Feature • a more accurate modeling of the dependencies • between the query terms • Query concepts • n-grams, term proximities, noun phrases, • named entities • verbose natural language queries • (grammatical complexity)

42. Example • Provide information on the use of dogs worldwide for law • enforcement purposes. sequential dependence model (dog, “law enforcement”) (information, “lawenforcement”)

43. Hypergraph structure Query: “ international art crime “

44. Evaluation