The SemLink resource provides mappings between a variety of lexical semantic ontologies, each with their strengths and weaknesses.
Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP.
This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items.
Guided by conceptual metaphor theory, we propose to control the generation process by encoding conceptual mappings between cognitive domains to generate meaningful metaphoric expressions.
We therefore adapt the DirectRanker to provide a new deep model for ranking creative language with small data.
This work describes the task of metaphoric paraphrase generation, in which we are given a literal sentence and are charged with generating a metaphoric paraphrase.
As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions.
We show that feature-based and deep learning methods provide different benefits for tweet classification, and ensemble-based methods using linguistic, temporal, and geospatial features can effectively classify user behavior.
Identification of metaphoric language in text is critical for generating effective semantic representations for natural language understanding.