![]() ![]() As expected, we find that debate reaction ideal points are more extreme among respondents who strongly identify with a political party, but retain substantial within-party variation. Debate reaction ideal points provide a method for estimating a continuous, individual-level measure of ideology that avoids survey response biases, provides better estimates for moderates and the politically unengaged, and reflects the content of salient political discourse relevant to viewers’ attitudes and vote choices. Using these reactions as inputs to an ideal point model, we estimate individual-level ideology and evaluate the quality of the measure. ![]() We extend the application of ideal point models to the public using a novel data source: real-time reactions to statements by candidates in the 2012 presidential debates. Ideal point models have become a powerful tool for defining and measuring the ideology of many kinds of political actors, including legislators, judges, campaign donors, and members of the general public. ![]() Read More about Automated Topic Model Evaluation Broken? The Incoherence of Coherence Automated evaluation will declare one model significantly different from another when corresponding human evaluations do not, calling into question the validity of fully automatic evaluations independent of human judgments. We use automatic coherence along with the two most widely accepted human judgment tasks, namely, topic rating and word intrusion. Using two of the most widely used topic model evaluation datasets, we assess a dominant classical model and two state-of-the-art neural models in a systematic, clearly documented, reproducible way. We address both the standardization gap and the validation gap. In addition, as we show via a meta-analysis of topic modeling literature, there is a substantial standardization gap in the use of automated topic modeling benchmarks. At the same time, unlike classical models, the practice of neural topic model evaluation suffers from a validation gap: automatic coherence for neural models has not been validated using human experimentation. Recent models relying on neural components surpass classical topic models according to these metrics. However, the field has coalesced around automated estimates of topic coherence, which rely on the frequency of word co-occurrences in a reference corpus. Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. Our department has close ties to the Computational Linguistics and Information Processing Laboratory (CLIP Lab) at UMD's Institute for Advanced Computer Studies, where colleagues from linguistics, computer science and the College of Information Studies (iSchool) work together to advance the state of the art in such areas as machine translation, automatic summarization, information retrieval, question answering and computational social science. These two strands of computational linguistics are connected by shared methods (such as Bayesian models), a shared concern with grounding theories in naturally occurring linguistic data and a shared view of language as a fundamentally computational system for which formally explicit models and theories can be specified, designed and tested. Researchers at Maryland have particular interests in using models to investigate problems in phonetics and phonology, psycholinguistics and language acquisition.Ĭomputational linguistics also has a practical side, sometimes referred to as "natural language processing" or "human language technology.” Here the goal is to make computers smarter about human language, improving the automated analysis and generation of text, with results that can interact effectively with other information systems. The first, known as "computational psycholinguistics,” uses computational models to better understand how people understand, generate and learn language and to characterize the human language capacity as a formal computational system. Computational linguistics at Maryland has two aspects.
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