Karadeniz, TalhaDogdu, Erdogan2020-04-132025-09-182020-04-132025-09-182018Dogdu, Erdogan; Karadeniz, Talha, "Improvement of General Inquirer Features with Quantity Analysis", 2018 IEEE International Conference on Big Data (Big Data), pp. 2228-2231, (2018).97815386503562639-1589https://doi.org/10.1109/BigData.2018.8622369https://hdl.handle.net/20.500.12416/15116Baidu; et al.; Expedia Group; IEEE; IEEE Computer Society; Squirrel AI LearningGeneral Inquirer is a word-affect association vocabulary having 11896 entries. Ranging from rectitude to expressiveness, it comes with a flavor of categories. Despite the extensive content, a mapping from "To be or not to be." to "How much?" can be beneficial for word representation. In this work, we apply a method of window based analysis to obtain real valued General Inquirer attributes. Sentence Completion task is chosen to calculate the effectiveness of the operation. After whitening post-process, total cosine similarity convention is followed to concentrate on embedding improvement. Results indicate that our quantity focused variant is considerable.eninfo:eu-repo/semantics/closedAccessSentence CompletionWord EmbeddingConference Object10.1109/BigData.2018.86223692-s2.0-85062601541