The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change
This tool addresses the need for efficient and standardized semantic annotation in linguistics and NLP, though it is incremental as it builds on existing models and methods.
The authors tackled the problem of measuring semantic proximity and word senses by developing DURel, an online tool that supports both human and computational annotation using Word-in-Context models, with results including clustered judgments and analysis features for sense frequency and change over time.
We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface. The tool supports standardized human annotation as well as computational annotation, building on recent advances with Word-in-Context models. Annotator judgments are clustered with automatic graph clustering techniques and visualized for analysis. This allows to measure word senses with simple and intuitive micro-task judgments between use pairs, requiring minimal preparation efforts. The tool offers additional functionalities to compare the agreement between annotators to guarantee the inter-subjectivity of the obtained judgments and to calculate summary statistics giving insights into sense frequency distributions, semantic variation or changes of senses over time.