English Out-of-Vocabulary Lexical Evaluation Task
This addresses a domain-specific challenge in natural language processing for handling OOV words, but it is incremental as it builds on existing embedding methods.
The paper tackles the problem of out-of-vocabulary (OOV) lexical evaluation by introducing tasks for classification and prediction without prior knowledge, using unsupervised word embedding methods like Word2Vec and Word2GM for baseline experiments.
Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge. The OOV words are words that only appear in test samples. The goal of tasks is to provide solutions for OOV lexical classification and prediction. The tasks require annotators to conclude the attributes of the OOV words based on their related contexts. Then, we utilize unsupervised word embedding methods such as Word2Vec and Word2GM to perform the baseline experiments on the categorical classification task and OOV words attribute prediction tasks.