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AL4Trust - Artificial Intelligence for Building Trust
The document outlines the AI4Trust master's program at Universitat Politècnica de València and discusses eight transformative changes in humanity driven by artificial intelligence and big data. It emphasizes the importance of language variety identification for improving geotagging and addresses the effectiveness of different machine learning models for tasks such as age and gender identification. Additionally, it presents the development and contributions of a low dimensionality representation approach, which significantly enhances accuracy in analyzing linguistic variations.
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AL4Trust - Artificial Intelligence for Building Trust
- 1. AI4Trust ARTIFICIAL INTELLIGENCE FOR BUILDINGTRUST MASTER’S DEGREE IN ARTIFICIAL INTELLIGENCE, PATTERN RECOGNITION AND DIGITAL IMAGING UNIVERSITAT POLITÈCNICA DE VALÈNCIA March 2, 2017 Francisco Manuel Rangel Pardo
- 2. AI4Trust-MIARFIDALC2017autoritas 8 changes thatwill transform humanity forever 1. Rewrite genetics 2. Discover new materials 3. Govern a disenchanted world 4. Teach machines to learn 5. Find a universal energy 6. Win to infections 7. Crop with little water 8. Life on Mars
- 3. autoritas 8 changes thatwill transform humanity forever 1. Rewrite genetics 2. Discover new materials 3. Govern a disenchanted world 4. Teach machines to learn 5. Find a universal energy 6. Win to infections 7. Crop with little water 8. Life on Mars AI4Trust-MIARFIDALC2017
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- 7. autoritas We need toanswer questions... … to know what questions to ask AI4Trust-MIARFIDALC2017
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- 14. autoritas Approx. only 2%of contents are geotagged!! AI4Trust-MIARFIDALC2017
- 15. autoritas Language variety identificationto improve geotagging ----> Later on AI4Trust-MIARFIDALC2017
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- 17. autoritas I really wantCataluña to be #independent!! Is the following sentence positive, negative, neutral or none? AI4Trust-MIARFIDALC2017
- 18. autoritas I really wantCataluña to be #independent!! Is the following sentence positive, negative, neutral or none? It depends on the subjectivity of the receiver, e.g. - Positive for Puigdemont - Negative for Rajoy AI4Trust-MIARFIDALC2017
- 19. autoritas I really wantCataluña to be #independent!! Is the following sentence positive, negative, neutral or none? It depends on the subjectivity of the receiver, e.g. - Positive for Puigdemont - Negative for Rajoy But what about the stance of the author with respect to the main topic? AI4Trust-MIARFIDALC2017
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- 22. autoritas https://www.youtube.com/watch?v=YrqMEn-5Pi8 - Retrieve andstore - Evolution - Words and topics - Labelling - Hashtags - People - Locations - Brands - Polarity, stance - Users, relationships - Gender, age - Author profile - ... BIG DATA IS A BIG PROBLEM… AND A BIG OPPORTUNITY tweets/second tweets/minute tweets/hour tweets/day AI4Trust-MIARFIDALC2017
- 23. autoritas What’s the profileof your organisation community? AI4Trust-MIARFIDALC2017
- 24. autoritas Political parties What’s theaffinity among political parties and media? Media AI4Trust-MIARFIDALC2017
- 25. autoritas Some applied technologies -Age & gender identification ----> PAN@CLEF; EmoGraph - Language variety identification ----> LDR + Word2Vec - Language variety & gender identification ---> PAN 2017 - Stance & gender detection ----> IberEval 2017 AI4Trust-MIARFIDALC2017
- 26. autoritas Language Variety Identification Languagevariety identification aims to detect linguistic variations in order to classify different varieties of the same language. Language variety identification may be considered an author profiling task, besides a classification one, because the cultural idiosyncrasies may influence the way users use the language (e.g. different expressions, vocabulary…). AI4Trust-MIARFIDALC2017
- 27. autoritas Differences with thestate of the art To discriminate between different varieties of the same language, but with the following differences: - We focus on different varieties of Spanish, although we tested our approach also with a different set of languages. - Instead of n-gram based representations, we propose a low dimensionality representation which is helpful when dealing with big data in social media. - We evaluate the proposed method with an independent test set generated from different authors in order to reduce possible overfitting. - We make available our dataset to the research community. (https://github.com/autoritas/RD-Lab/tree/master/data/HispaBlogs) AI4Trust-MIARFIDALC2017
- 28. autoritas A Low DimensionalityRepresentation (LDR) Step 1. Term-frequency - inverse document frequency (tf-idf) matrix: Step 2. Class-dependent term weighting: Step 3. Class-dependent document representation: - Each column is a vocabulary term t - Each row is a document d - wij is the tf-idf weight of the term j in the document i - represents the assigned class c to the document AI4Trust-MIARFIDALC2017
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- 30. autoritas Alternative representations - Weuse the common state-of-the-art representations based on n-grams. We iterated n from 1 to 10, and selected the 1000, 5000 and 10000 most frequent n-grams. The best results were obtained with: - character 4-grams; the 10,000 most frequent - word 1-gram (bag-of-words); the 10,000 most frequent - word 2-grams; the 10,000 highest tf-idf - Two variations of the continuous Skip-gram model (Mikolov et al.): - Skip-grams - Sentence Vectors Maximizing the average of the log probability: Using the negative sampling estimator: AI4Trust-MIARFIDALC2017
- 31. autoritas Hispablogs https://github.com/autoritas/RD-Lab/tree/master/data/HispaBlogs - - Completely independentauthors between training and test sets - - Manually collected by social media experts of Autoritas AI4Trust-MIARFIDALC2017
- 32. autoritas Accuracy results withdifferent machine learning algorithms Significance of the results wrt. the two systems with the highest performance AI4Trust-MIARFIDALC2017
- 33. autoritas The effect ofthe pre-processing Accuracy obtained after removing words with frequency equal or lower than n (a) Continuous scale (b) Non-continuous scale AI4Trust-MIARFIDALC2017
- 34. autoritas The effect ofthe pre-processing Number of words after removing those with frequency equal or lower than n, and some examples of very infrequent words. AI4Trust-MIARFIDALC2017
- 35. autoritas Evaluation results * ** * ** Representation Accuracy(%) Skip-gram 72,2 LDR 71,1 SenVec 70,8 BOW 52,7 Char 4-grams 51,5 EmoGraph 39,3 tf-idf 2-grams 32,2 Random baseline 20,0 AI4Trust-MIARFIDALC2017
- 36. autoritas Error analysis Confusion matrixof the 5-class classification F1 values for identification as the corresponding language variety vs. others AI4Trust-MIARFIDALC2017
- 37. autoritas Features contribution Accuracy obtainedwith different combinations of features AI4Trust-MIARFIDALC2017
- 38. autoritas Cost analysis Complexity ofobtaining the features: Number of features: Representation # Features LDR 30 Skip-gram 300 SenVec 300 EmoGraph 1,100 BOW 10,000 Char 4-grams 10,000 tf-idf 2-grams 10,000 l: number of varieties n: number of terms of the document m: number of terms in the document that coincides with some term in the vocabulary n m & l<<n AI4Trust-MIARFIDALC2017
- 39. autoritas Robustness Results obtained with thedevelopment set of the DSLCC corpus from the Discriminating between Similar Languages task (2015) NOTE: Significant results in bold AI4Trust-MIARFIDALC2017
- 40. autoritas LDR for ageand gender identification Dataset Genre Lang Age Pos. Gender Pos. Nº Partici. EmoGraph LDR EmoGraph LDR PAN-AP-2013 Social Media ES 66,24* 62,70 3 63,65* 60,75 6 21 PAN-AP-2014 Social Media ES 45,9 38,16 6 68,6* 56,89 9 9 PAN-AP-2014 Social Media EN 34,2* 31,63 6 53,4 51,42 9 10 PAN-AP-2014 Blogs ES 46,4 46,43 3 64,3 50,00 5 9 PAN-AP-2014 Blogs EN 46,2 38,46 3 71,3 67,95 1 10 PAN-AP-2014 Twitter ES 58,9 56,67 2 73,3 63,33 2 8 PAN-AP-2014 Twitter EN 45,5 52,60 1 72,1 67,53 3 9 AI4Trust-MIARFIDALC2017
- 41. autoritas Conclusions LDR outperforms commonstate-of-the-art representations by 35% increase in accuracy. LDR obtains competitive results compared with two distributed representation-based approaches that employed the popular continuous Skip-gram model. LDR remains competitive with different languages and media (DSLCC). The dimensionality reduction is from thousands to only 6 features per language variety. This allows to deal with big data in social media. We have applied LDR to age and gender identification with competitive results with respect to the well-behaved EmoGraph. AI4Trust-MIARFIDALC2017
- 42. autoritas HispaTweets https://github.com/autoritas/RD-Lab/tree/master/doc/projects/Identificacion-de-la-Variedad-del-Lenguaje-para-la-Mejora-del-Geoposicionamiento-en-Social- Media - 650 authorsper language variety - 865 tweets per author (avg) - 7 Spanish varieties: - Argentina - Chile - Colombia - Mexico - Peru - Spain - Venezuela AI4Trust-MIARFIDALC2017
- 43. autoritas PAN-AP 2017 GENDER ANDLANGUAGE VARIETY IDENTIFICATION ENGLISH SPANISH PORTUGUESE ARABIC ● Australia ● Canada ● Great Britain ● Ireland ● New Zealand ● United States ● Argentina ● Chile ● Colombia ● Mexico ● Peru ● Spain ● Venezuela ● Brazil ● Portugal ● Egypt ● Gulf ● Levantine ● Maghrebi http://pan.webis.de/clef17/pan17-web/author-profiling.html - 500 authors per gender and language variety - 100 tweets per author AI4Trust-MIARFIDALC2017
- 44. autoritas IberEval 2017 AI4Trust-MIARFIDALC2017 http://stel.ub.edu/Stance-IberEval2017/index.html GENDER ANDSTANCE DETECTION WRT. INDEPENDENCE OF CATALONIA ● Tweets in Spanish and Catalan ● Annotated with ○ Gender (male / female) ○ Stance (favor / against / none) Language: Catalan Stance: FAVOR Gender: FEMALE Tweet: "15 diplomàtics internacional observen les plebiscitàries, serà que interessen a tothom menys a Espanya #27S" (‘15 international diplomats observe the plebiscite, perhaps it is of interest to everybody except to Spain#27S’)