Language and Text Processing
Rada Mihalcea receives Distinguished Faculty Achievement AwardMihalcea is being recognized for her contributions to computational linguistics and her efforts to broaden participation in the field of computer science.
Paper by U-M researchers selected for Best Paper in IEEE Transactions on Affective ComputingThe research on automatic speech emotion recognition is one of the five papers featured in the collection.
Student awarded NSF Fellowship for automating speech-based disease classification
Perez’s research focuses on analyzing speech patterns of patients with Huntington Disease.
Paper award for identifying speaker characteristics in text messages
The goal of the work was to identify seven things about who the subject was talking to just by analyzing text messages.
Gaining a deeper understanding of how personal values are expressed in text
Researchers used hierarchical trees to provide a better idea of how concepts are represented and related in a collection of text.
Detecting Huntington’s disease with an algorithm that analyzes speech
New, preliminary research found automated speech test accurately diagnoses Huntington’s disease 81 percent of the time and tracks the disease’s progression.
Fake news detector algorithm works better than a human
System sniffs out fakes up to 76 percent of the time.
Chat tool simplifies tricky online privacy policies
Automated chatbot uses artificial intelligence to weed through fine print
Emotions predicted by examining the correlation between tweets and environmental factors
External factors, ranging from weather, news exposure, social network emotion charge, timing, and mood predisposition may have a bearing on one’s emotion level throughout the day.
Improving natural language processing with demographic-aware models
Word associations vary across different demographics, allowing researchers to build better natural language processing models if they can account for demographics.
Rada Mihalcea co-authors new book on text mining
Text Mining brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections.
U-M, IBM partner on advanced conversational computing system
The project aims to develop a cognitive system that functions as an academic advisor for undergraduate computer science and engineering majors at the university.
Lie-detecting software uses real court case data
U-M researchers are building a unique lie-detecting software that works from studying real world data from real, high-stakes court cases.