LT-Accelerate is Europe's premier event for text, speech, and social technologies and their business applications, 21-22 November, 2016 in Brussels. Our pictures are available here. 21 November • Monday |
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09.00 |
Language Technologies, Futures, and Routes to Market |
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09.30 |
FutureTDM: Improving Uptake of Text and Data Mining in the EU |
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10.00 |
Between Custom and Off-the-shelf NLP |
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10.30 |
Coffee Break |
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11.00 |
SpaCy and NLP Innovation |
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11.30 |
Turn Email into Data with Deep Learning, Plus Other Industry Tasks with Gensim Topic Modeling |
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12.00 |
Conversational Data at Scale, at Skype |
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12.30 |
Walking Lunch & Networking (Asian buffet) |
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13.30 |
Panel: Social Data Science |
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14.10 |
Superimposing Emotional and Psychographic Constructs on Digital Media |
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14.30 |
Brand and Product Insights via Review Text Analytics and Reviewer Profiling |
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14.50 |
Leveraging Social Conversations for Effective AI Training |
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15.00 |
Coffee Break |
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15.30 |
Panel: Analytics and Decision Making from Heterogeneous Content |
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16.30 |
Social Listening & Analytics 3.0 |
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17.00 |
Networking & Drinks |
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22 November • Tuesday |
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09.00 |
Welcome |
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09.10 |
Panel: Speech and Language Technologies for Media Processing |
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10.10 |
Fraunhofer Audio Mining: Solution for Broadcast Archiving |
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10.30 |
Coffee Break |
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11.00 |
Text & Social Analytics for Research & Insights |
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11.30 |
How emotions drive customer relationships – a case study about personal banking in the US |
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12.00 |
Capturing Mixed Feelings with Fine Grained Sentiment Analysis |
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12.20 |
CoderRank: Creating Gold Standards |
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12.30 |
Walking Lunch & Networking (Autumn buffet) |
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13.30 |
Data Conversation, Reinventing the Analysis Interface |
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13.50 |
Conversational Interaction for Business |
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14.20 |
Healthcare IT and NLG: Great synergies ahead? |
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14.50 |
Transforming the Translation Environment – a Paradigm Shift in Productivity |
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15.00 |
Coffee Break |
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15.30 |
Exploiting Market Sentiment to Create Daily Trading Signals |
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16.00 |
Classification and Estimation of Finance Relevant Topics in Social Media |
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16.30 |
Assessing Consumer Financial Complaints using Text Analytics and Machine Learning |
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16.50 |
Closure |
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17.00 |
End |