Natural Language Processing Poster Presentations
Join us for the final project poster presentations from the course Info 159/259. Natural Language Processing.
This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). The class focuses on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students implement algorithms and create linguistically annotated data on which those algorithms depend.
Posters include:
- SignNet: End-to-End ASL to Text Translation
- Analysis of Media Framing in News
- Let's Fool the Sentiment Classifier
- Measuring Earnings Calls Off-Scriptedness
- Aspect-opinion Aware Recommendation Over Point of Interests Based on User Reviews
- Text classification and Information Extraction for Crash Descriptions
- Commitment Search for Facilitating Fact-Checking
- Reconstructing Cuneiform Lacunae
- Text-Based Journal Prediction
- Conversational Agent for Sports Press Conferences
- Black Box Adversarial Learning for Sentiment Classification
- Authorship Attribution Using Distributed Representation
- Predicted Stock Price Movements with Financial News Sentiments
- Connecting Concepts in Science Fiction to Wikipedia and Modelling the Evolution of the Science Fiction Genre