Peter Potash

GhostWriter: Using an LSTM for Automatic Rap Lyric Generation

This paper demonstrates the effectiveness of a Long Short-Term Memory language model in our initial efforts to generate unconstrained rap lyrics. The goal of this model is to generate lyrics that are similar in style to that of a given rapper, but not identical to existing lyrics: this is the task of ghostwriting. Unlike previous work, which defines explicit templates for lyric generation, our model defines its own rhyme scheme, line length, and verse length. Our experiments show that a Long Short-Term Memory language model produces better “ghostwritten” lyrics than a baseline model.

TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis

This paper describes TwitterHawk, a system for sentiment analysis of tweets which participated in the SemEval-2015 Task 10, Subtasks A through D. The system performed competitively, most notably placing 1 st in topicbased sentiment classification (Subtask C) and ranking 4 th out of 40 in identifying the sentiment of sarcastic tweets. Our submissions in all four subtasks used a supervised learning approach to perform three-way classification to assign positive, negative, or neutral labels. Our system development efforts focused on text pre-processing and feature engineering, with a particular focus on handling negation, integrating sentiment lexicons, parsing hashtags, and handling expressive word modifications and emoticons. Two separate classifiers were developed for phrase-level and tweetlevel sentiment classification. Our success in aforementioned tasks came in part from leveraging the Subtask B data and building a single tweet-level classifier for Subtasks B, C and D.

Combining Network and Language Indicators for Tracking Conflict Intensity.

This work seeks to analyze the dynamics of social or political conflict as it develops over time, using a combination of network-based and language-based measures of conflict intensity derived from social media data. Specifically, we look at the random-walk based measure of graph polarization, text-based sentiment analysis, and the corresponding shift in word meaning and use by the opposing sides. We analyze the interplay of these views of conflict using the Ukraine-Russian Maidan crisis as a case study.

SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity

This paper describes the SimiHawk system submission from UMass Lowell for the core Semantic Textual Similarity task at SemEval2016. We built four systems: a small featurebased system that leverages word alignment and machine translation quality evaluation metrics, two end-to-end LSTM-based systems, and an ensemble system. The LSTMbased systems used either a simple LSTM architecture or a Tree-LSTM structure. We found that of the three base systems, the feature-based model obtained the best results, outperforming each LSTM-based model’s correlation by .06. Ultimately, the ensemble system was able to outperform the base systems substantially, obtaining a weighted Pearson correlation of 0.738, and placing 7th out of 115 participating systems. We find that the ensemble system’s success comes largely from its ability to form a consensus and eliminate complementary noise from its base systems’ predictions.