Transforming dense, unstructured text into interpretable tables-commonly referred to as Text-to-Table generation-is a key task in information extraction.Existing methods often overlook what complex information to extract and how to infer it from text.We present Map&Make, a versatile approach that decomposes text into atomic propositions to infer latent schemas, which are then used to generate tables capturing both qualitative nuances and quantitative facts.We evaluate our method on three challenging datasets: Rotowire, known for its complex, multi-table schema; Livesum which requires numerical aggregation; and Wiki40 which require open text extraction from mulitple domains.By correcting hallucination errors in Rotowire, we also provide a cleaner benchmark.Our method shows significant gains in both accuracy and interpretability across comprehensive comparative and referenceless metrics.Finally, ablation studies highlight the key factors driving performance and validate the utility of our approach in structured summarization.Code and data are available

Hashtag segmentation is the task of breaking a hashtag into its constituent tokens. Hashtags often encode the essence of user-generated posts, along with information like topic and sentiment, which are useful in downstream tasks. Hashtags prioritize brevity and are written in unique ways - transliterating and mixing languages, spelling variations, creative named entities. Benchmark datasets used for the hashtag segmentation task - STAN, BOUN - are small and extracted from a single set of tweets. However, datasets should reflect the variations in writing styles of hashtags and account for domain and language specificity, failing which the results will misrepresent model performance. We argue that model performance should be assessed on a wider variety of hashtags, and datasets should be carefully curated. To this end, we propose HashSet, a dataset comprising of: a) 1.9k manually annotated dataset; b) 3.3M loosely supervised dataset. HashSet dataset is sampled from a different set of tweets when compared to existing datasets and provides an alternate distribution of hashtags to build and validate hashtag segmentation models. We analyze the performance of SOTA models for Hashtag Segmentation, and show that the proposed dataset provides an alternate set of hashtags to train and assess models.

We report the results of a crowdsourcing user study for evaluating the effectiveness of human-chatbot collaborative conversation systems, which aim to extend the ability of a human user to answer another person's requests in a conversation using a chatbot. We examine the quality of responses from two collaborative systems and compare them with human-only and chatbot-only settings. Our two systems both allow users to formulate responses based on a chatbot's top-ranked results as suggestions. But they encourage the synthesis of human and AI outputs to a different extent. Experimental results show that both systems significantly improved the informativeness of messages and reduced user effort compared with a human-only baseline while sacrificing the fluency and humanlikeness of the responses. Compared with a chatbot-only baseline, the collaborative systems provided comparably informative but more fluent and human-like messages.

Abstract Cloud computing is the delivery of on‐demand computing resources. Cloud computing has numerous applications in fields of education, social networking, and medicine. But the benefit of cloud for medical purposes is seamless, particularly because of the enormous data generated by the health care industry. This colossal data can be managed through big data analytics, and hidden patterns can be extracted using machine learning procedures. In particular, the latest issue in the medical domain is the prediction of heart diseases, which can be resolved through culmination of machine learning and cloud computing. Hence, an attempt has been made to propose an intelligent decision support model that can aid medical experts in predicting heart disease based on the historical data of patients. Various machine learning algorithms have been implemented on the heart disease dataset to predict accuracy for heart disease. Naïve Bayes has been selected as an effective model because it provides the highest accuracy of 86.42% followed by AdaBoost and boosted tree. Further, these 3 models are being ensembled, which has increased the overall accuracy to 87.91%. The experimental results have also been evaluated using 10,082 instances that clearly validate the maximum accuracy through ensembling and minimum execution time in cloud environment.