Papers by Alex Rosenfeld
Deep Neural Models of Semantic Shift (N18-1)
Copied to clipboard
| Challenge: | Diachronic distributional models track changes in word use over time using a continuous variable and a synthetic task to measure the semantic trajectory of a word. |
| Approach: | They propose a deep neural network diachronic distributional model that represents time as a continuous variable and model a word’s usage as . a synthetic task which measures how well a model captures the semantic trajectory of a . word over time. |
| Outcome: | The proposed model can capture the semantic trajectory of a word over time and can measure the speed of lexical change. |
Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis (D19-1)
Copied to clipboard
| Challenge: | a new study examines the use of labeled and unlabeled corpora in political science research . large corporata often contain documents of a certain subject or type, but they are often unlabed . a recent study found that labeles with pertinent documents stem from a single source . |
| Approach: | They propose an unsupervised domain adaptation framework that uses a text classification model and time-aware training to ensure it works well with diachronic corpora. |
| Outcome: | The proposed framework outperforms benchmarks on an expert-annotated dataset and is more stable and learns better representations. |