We study how to automatically generate cloze questions from given texts to assess reading comprehension, where a cloze question consists of a stem with a blank space holder for the answer key, and three distractors for generating confusions. We present a generative method called CQG (Cloze Question Generator) for constructing cloze questions from a given article, utilizing neural networks and WordNet with an emphasis on generating multi-gram distractors. WordNet is a hypernym-hyponym network of synsets, where each synset is a set of lemmas with the same gloss labeled by a lexname. CQG harnesses word-sense disambiguation, text-to-text transformation, and WordNet's synset taxonomies and lexical labels to select an answer key from a sentence, segment it, and generate instance-level distractor candidates (IDCs) using a transformer and sibling synsets. After ranking the IDCs based on contextual embedding similarities, synset, and lexical relatedness, CQG forms distractor candidates and checks if they align with people's writing conventions to determine whether they can be distractors. CQG significantly outperforms SOTA results, confirmed by the high quality of the generated distractors assessed by human judges. The effectiveness of CQG, however, is confined by WordNet's limited vocabulary. There are tens of thousands of new lemmas that are not yet included in WordNet. It is therefore desirable to construct an automated system that can add new lemmas to WordNet with their glosses being the only information available, which can be readily obtained from Wikitionary and other open sources. This task is challenging. We tackle this challenge by devising a system called WordNeter that predicts a lexname for the given gloss, determines if a new synset should be formed, predicts a hypernym for the new synset, and updates the existing hypernym-hyponym relations in WordNet. WordNeter excels with a 93.6% F1 score on predicting lexnames for given glosses and 64.8% exact matches of the predicted direct hypernyms with the true direct hypernyms, which significantly outperforms GPT-3.5-Turbo and other models. Even without exact matches of hypernym predictions, most predicted hypernyms are still suitable for generating high-quality distractors. Integrating WordNeter with CQG expands CQG's ability to generate satisfactory distractors for cloze questions with answer keys outside WordNet's current vocabulary, advancing the methodology of cloze question generation. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]