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At the Intersection of Data Science and Language
Published on Nov 10, 20161854 Views
Data science holds the promise to solve many of society’s most pressing challenges, but much of the necessary data is locked within the volumes of unstructured data on the web including language, spee
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Chapter list
At the Intersection of Language and Data Science00:00
New Media For Data Science02:11
Vision02:24
Machine learning framework - 102:39
Machine learning framework - 203:10
Machine learning framework - 303:35
Categories researched - 103:47
Categories researched - 204:44
News04:51
Problem: Identifying needs during disaster 05:38
Monitor events over time06:37
Track events and SubEvents07:00
Data from NIST: 2011 – 2013 Web Crawl, 11 categories 07:29
Data Collected over the Course of the Hour - 108:09
Data Collected over the Course of the Hour - 208:18
Data Collected over the Course of the Hour - 308:19
Data Collected over the Course of the Hour - 408:20
Data Collected over the Course of the Hour - 508:31
Temporal Summarization Approach08:58
Predicting Salience: Model Features - 109:48
Predicting Salience: Model Features - 210:34
Predicting Salience: Model Features - 311:03
Predicting Salience: Model Features - 411:23
Predicting Salience: Model Features - 511:45
What Have We Learned? - 112:16
What’s next? - 213:02
Categories researched - 313:35
Wikipedia13:42
How do Wikipedia descriptions differ?14:06
RDF Applications - 114:38
RDF Applications - 214:55
RDF Applications - 315:35
Paraphrasal Template Extraction16:32
Sentences from the corpus 17:16
Entities/dates identified17:30
Types replace entities/dates - 118:17
Types replace entities/dates - 218:50
Types replace entities/dates - 319:26
Hybrid Approach - 119:42
Hybrid Approach - 220:20
Evaluation20:40
What have we learned? - 221:38
What have we learned? - 322:06
What’s next? - 222:31
Personal Narrative - 122:59
Personal Narrative - 223:03
How is Personal Narrative Different?23:35
Personal Views - 124:05
Personal Views - 224:28
Personal Views - 324:38
Personal Views - 424:56
Personal Views - 525:29
Identify the Reportable Event25:48
Data26:14
Linguistic Theory27:23
Sentance Scores27:49
Features: Change in Affect 28:43
What have we learned? - 429:16
What's next? - 330:06
Social Media30:34
How is social media different30:39
Problem31:12
Gang Violence & Social Media31:42
Can we automatically detect aggressive posts?32:07
Approach32:47
Case Study34:07
Themes That Emerge From Coding35:30
Qualitative Analysis36:10
Natural Language Tools 37:02
Aggression/Loss Classifier 37:51
What have we learned? - 538:49
Novels - 139:42
Novels - 239:45
Novels - 340:03
Novels - 440:26
Computer Science and Comparative Literature40:43
Hypothesis #141:27
Constructing the Social Network42:33
Impact of Network Size - 143:15
Impact of Network Size - 243:50
Impact of Network Size - 344:23
Alternate Explanation45:04
3rd Person Narrative45:40
“Close 3rd” Narrative46:05
1st Person Narrative46:27
What have We Learned? - 546:41
What have We Learned? - 647:32
What have We Learned? - 747:54
What have We Learned? - 848:06
Current PhD Students48:22
Past Students48:33
Thank You!48:43