Word vector-space embeddings of natural language data over time
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Words are often mapped to vectors in a vector-space (Euclidean-space). Such mappings, also called embeddings, are used in many Natural Language Processing (NLP) tasks. These word embeddings are, generally, intended to reflect the usage, semantic similarities and relatedness of the words they represent. Simply put, word embeddings reflect the meaning of the words relative to other words. However, word meanings are known to change over time (semantic change). Current publicly available word vector-space embeddings are ‘static’ in nature with no temporal component. Creating ‘dynamic’ word embeddings by adding temporal information opens the possibility of capturing the phenomenon of semantic change. These embeddings (with temporal component) can be used to produce visual animation of semantic change and change in word relations over time. It also has the potential to improve performance of various NLP tasks, particularly those involving time like the task of Diachronic Text Evaluation. This project achieves the following: (1) Create word embeddings with time component (dynamic embeddings) that captures the meaning/usage/similarities of words across various times ranging between the years 1800 and 2008. (2) Develop a tool/software that animates changes in word relations using the dynamic embeddings. (3) Evaluate the dynamic embeddings created using word similarity measures and Diachronic Text Evaluation task.