Some notes on the needs washed construction

This paper describes the geographic and social factors that correlate with the acceptability of the needs washed construction, based on the results of recent survey data. After briefly describing the survey methods, we discuss several ways to analyze the geographic distribution of the construction, focusing on the distribution of “hot” and “cold” spots across different versions of the construction. We find certain core areas where the construction is highly accepted, as well as core areas where the construction is highly rejected. Our survey looks at the effect of verb (need, want, like, and love), tense/modality (finite verb, modal would and would have), and population density (urban, suburban, rural). Moreover, we present maps that show how our results line up with previously proposed dialect regions of American English

Mapbook of Syntactic Variation in American English: Survey Results, 2015–2019

This work presents the results of a series of acceptability judgment surveys conducted by the Yale Grammatical Diversity Project (YGDP) between 2015 and 2019. It contains over 200 maps of some 194 sentences, covering a wide range of syntactic constructions, including dative presentatives, personal datives, extended benefactives, the have yet to construction, the done my homework construction, wicked, hella, the so don’t I construction, the alls construction, the come with construction, fixin’ to, the needs washed construction, non-polarity anymore (aka “positive anymore”), and many others. For each sentence, we also provide some basic demographic information, such as how the sentence judgments varied by age, race, gender, education, and urban/rural classifications. We describe the goals of these surveys, as well as how they were designed, administered, processed, and mapped, along with a brief introduction to the history of the YGDP. In addition to providing a detailed look at syntactic variation in U.S. English to an extent that has previously been unavailable, we hope that this work will be useful in linguistics classrooms at all levels, and will provide the springboard for further, more detailed studies of the individual constructions, geographic regions, and linguistic and social factors connected to syntactic variation in U.S. English

Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream