We present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets (representing event types) and inducing their temporal order. Our model exploits semantic and positional similarity and allows for flexible event order, thus overcoming the rigidity of previous approaches. We incorporate crowdsourced alignments as prior knowledge and show that exploiting a small number of alignments results in a substantial improvement in cluster quality over state-of-the-art models and provides an appropriate basis for the induction of temporal order. We also show a coverage study to demonstrate the scalability of our approach.

The need for Question Answering datasets in low resource languages is the motivation of this research, leading to the development of Kencorpus Swahili Question Answering Dataset, KenSwQuAD. This dataset is annotated from raw story texts of Swahili low resource language, which is a predominantly spoken in Eastern African and in other parts of the world. Question Answering (QA) datasets are important for machine comprehension of natural language for tasks such as internet search and dialog systems. Machine learning systems need training data such as the gold standard Question Answering set developed in this research. The research engaged annotators to formulate QA pairs from Swahili texts collected by the Kencorpus project, a Kenyan languages corpus. The project annotated 1,445 texts from the total 2,585 texts with at least 5 QA pairs each, resulting into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5% of the annotated texts confirmed that the QA pairs were all correctly annotated. A proof of concept on applying the set to the QA task confirmed that the dataset can be usable for such tasks. KenSwQuAD has also contributed to resourcing of the Swahili language.

Script knowledge consists of detailed information on everyday activities. Such information is often taken for granted in text and needs to be inferred by readers. Therefore, script knowledge is a central component to language comprehension. Previous work on representing scripts is mostly based on extensive manual work or limited to scenarios that can be found with sufficient redundancy in large corpora. We introduce the task of scenario detection, in which we identify references to scripts. In this task, we address a wide range of different scripts (200 scenarios) and we attempt to identify all references to them in a collection of narrative texts. We present a first benchmark data set and a baseline model that tackles scenario detection using techniques from topic segmentation and text classification.

Writing style change detection models focus on determining the number of authors of documents with or without known authors. Determining the exact number of authors contributing in writing a document particularly when the authors contribute short texts in form of a sentence is still challenging because of the lack of standardized feature sets able to discriminate between the works of authors. Therefore, the task of identifying the best feature set for all the tasks of the writing style change detection is still considered important. This paper sought to determine the best feature set for the writing style change detection tasks; separating documents with several style changes (multi-authorship) from documents without any style changes (single-authorship), and determining the number and location of style changes in the case of multi-authorship. We performed exploratory research on existing stylometric features to determine the best document level and sentence level features. Document level features were extracted and used to separate single authored from multi-authored documents, while sentence level features were used to answer the question of determining the number of style changes To answer this question, we trained a random forest classifier to rank document level features and sentence level features separately, and applied an ablation test on the top 15 sentence level features using k-means clustering algorithm to confirm the effect of these features on model performance. The study found out that the best document level feature set for separating documents with and without style change was provided by an ensemble of features including number of sentence repetitions (num_sentence_repetitions) as the most determinant feature, 5-grams, 4-grams, Special_character, sentence_begin_lower, sentence_begin_upper, diversity, automated_readability_index, parenthesis_count, first_word_uppercase, lensear_write_formula, dale_chall_readability, difficult_words, type_token_ratio. These were the top ranked features in experiment one. On the other hand, the top fifteen sentence level features based on feature ranks using random forest classifier were diversity, dale_chall_readability grade, check_available_vowel, flesch_kincaid grade, parenthesis_count, colon_count, verbs, bigrams, alphabets, personal pronouns, coordinating conjunctions, interjections, modals, type_token ratio and punctuations_count. Consequently, the optimal feature set for determining the number of style changes in documents was considered based on the results of the ablation study on the top fifteen sentence level features, and was provided by an ensemble of features including personal pronouns, check_available_vowels, punctuations_counts, parenthesis count, coordinating conjunctions and colon count.

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