Starbuck.pptx
- 2. Background Jamie and Debbie got married. A couple weeks after getting married, they left England and started travelling around the world. Jamie and Debbie stayed in touch with their family and friends via email. However, after about 2 years, the mother of Debbie started to worry about her. She contacted the police and said: Question: What do you think is the problem?
- 3. Their opinion We think that the emails we receive from Debbie are not written by Debbie. They though that the wording of the emails was not the type of wording that their daughter would use. Question: Would you family notice if you did not write your own email? Why do you think so?
- 5. Datasets The detectives at Nottingham police station collected the emails that the mother had received from Debbie’s email address. The emails were divided into two datasets: • Dataset 1: Emails received from Debbie before marriage • Dataset 2: Emails received from Debbie after marriage The detectives also felt that the style of writing was not the same in Dataset 1 and 2. They then collected emails sent by Jamie to his family. This is the third dataset. • Dataset 3: Emails received from Jamie
- 6. Dataset names The police now have three datasets. • Dataset 1: Emails received from Debbie before marriage • Dataset 2: Emails received from Debbie after marriage • Dataset 3: Emails received from Jamie The investigators also used another dataset containing thousands of emails written by different people. • Dataset 4: Large collection of emails from many different senders Question: Decide which datasets are questioned, known or reference.
- 7. Datasets Questioned (3,000 words) Known 1: Debbie (28,000 words) Known 2: Jamie (6,000 words) Reference (1 million words)
- 8. Datasets If the language features in two datasets are similar, the same person may have written them. If the language features in two datasets are NOT similar, the same person may NOT have written them. So, we need to discover if: The features in the questioned dataset are similar to a known corpus The features in the questioned dataset are different to a known corpus Question: How can we do this?
- 9. Answers Deep learning • This will probably work, but it is difficult to explain in court Statistical analysis • This may also work, but it is also difficult to explain in court Habits of language (idiosyncratic language - 口癖) • This works and is the easy to explain. Question: How can we systematically identify idiosyncratic language?
- 10. Is your language idiosyncratic? THIN FAT DELICIOUS I Thin Slim Slender Skeletal Skinny Emaciated Fat Plump Tubby Chubby Podgy Overweight Obese Delicious Tasty Yummy Flavorsome Delectable Scrumptious 俺 僕 私 儂 自分 うち あたし
- 11. Datasets Questioned Known 1: Debbie Known 2: Jamie Reference Afd affadsA Faasfsafs afadsfa Fsaafd affsafs Aafs adsf Afd affadsA Afd affadsA afadsfa Fsaafd affsafs adsf Afd affadsA Three common language features are used: 1. word frequency, 2. word frequency of words that are used more than expected, and 3. patterns following such words
- 12. Datasets Questioned Known 1: Debbie Known 2: Jamie Reference Afd affadsA Faasfsafs afadsfa Fsaafd affsafs Aafs adsf Afd affadsA Afd affadsA afadsfa Fsaafd affsafs adsf Afd affadsA Question: How do we identify word frequency?
- 13. Datasets Questioned Known 1: Debbie Known 2: Jamie Reference Afd affadsA Faasfsafs afadsfa Fsaafd affsafs Aafs adsf Afd affadsA Afd affadsA afadsfa Fsaafd affsafs adsf Afd affadsA Question: How do we identify frequency of words that are used more than expected (keywords)?
- 14. Datasets Questioned Known 1: Debbie Known 2: Jamie Reference Afd affadsA Faasfsafs afadsfa Fsaafd affsafs Aafs adsf Afd affadsA Afd affadsA afadsfa Fsaafd affsafs adsf Afd affadsA Question: How do we identify patterns following keywords?
- 15. Datasets Questioned Known 1: Debbie Known 2: Jamie Reference Awhile 3 Buisiness 2 was sat 1 Awhile 2 Buisiness 4 was sat 1 Question: If these keywords were discovered, what would you conclude?
- 17. Jamie Starbuck Check the result online by searching for “Jamie Starbuck”
- 18. System Needed
- 19. Expert system When preparing evidence for this case, the linguistic had to: • Analyze keywords in the Questioned dataset • Analyze keywords in each Known dataset separately • Create a table in Excel to compare the keywords • Identify keywords that occur in both Questioned and Known datasets An expert system is needed to streamline this process, and remove the chance of human error.