567 research outputs found
Food for nought
No full textThe short story, "Food for nought", is written by the listed author above, Shashi Bhat. Now in its 48th year, Best Canadian Stories has long championed the short story form and highlighted the work of many of the writers, throughout their respective careers, who have gone on to shape the Canadian literary canon. Caroline Adderson, Margaret Atwood, Clark Blaise, Lynn Coady, Mavis Gallant, Zsuzsi Gartner, Douglas Glover, Steven Heighton, Isabel Huggan, Mark Anthony Jarman, Norman Levine, Rohinton Mistry, Alice Munro, Leon Rooke, Diane Schoemperlen, Russell Smith, Linda Svendsen, Kathleen Winter, and many others have appeared in its pages over the years and decades, making Best Canadian Stories the go-to source for what’s new in Canadian fiction writing for close to five decades. A continuation of not only a series, but a legacy in Canadian letters. --From publisher description.Published
TWO NEW SPECIES OF POACEAE FROM INDIA
No full textTwo new species of Poaceae namely, Erayrostis santapaui K. G. Bhat & C. R. Nagendran and Chrysopogon pseitdozeylanicus K. G. Bhat & C. R. Nagendran have been described from materials collected by the senior author from Coorg- and South Kanara Districts of Karnataka State, India
Semantic modeling of the natural language of Wikipedia annotations
No full textKnowledge bases (KB) store relational facts and constitute a significant resource for a variety of natural language processing (NLP) tasks. Improving their coverage and refining the relations is a basic and pressing research effort. In this thesis we propose a novel approach towards this canonical task by using the unstructured Wikipedia corpus: we extract low-dimensional embeddings for title pages of the Wikipedia corpus and show that they can be used to significantly outperform state-of-the-art approaches on a variety of metrics in three concrete tasks: measuring semantic relatedness, solving semantic analogies, and KB completion and refinement. A central feature of our work is a new log-linear discriminative model for the annotations inside a Wikipedia document that we name IBOE (isotropic bag-of-entities): we hypothesize that the parameters of the model satisfy a geometric symmetry property (isotropy). We show that the isotropy property leads to self-normalization allowing for the design of an efficient parameter estimation algorithm that we christen wiki2vec. The self-normalization property of IBOE is validated empirically on the Wikipedia corpus and is also of independent mathematical interest.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-11-09 without embargo termsThe student, Jiaqi Mu, accepted the attached license on 2016-07-15 at 10:25.The student, Jiaqi Mu, submitted this Thesis for approval on 2016-07-15 at 10:39.This Thesis was approved for publication on 2016-07-15 at 13:08.DSpace SAF Submission Ingestion Package generated from Vireo submission #9961 on 2016-11-09 at 10:25:14Made available in DSpace on 2016-11-10T17:55:14Z (GMT). No. of bitstreams: 2
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Previous issue date: 2016-07-1
Tweeting Prejudice: Analyzing the Evolution of Attitudes Toward Asian Americans on Twitter During the COVID-19 Pandemic
No full textAfter COVID-19’s arrival in the United States, incidents of Asian hate skyrocketed. With anti-Asian rhetoric being spread on social media from political figures, like President Donald Trump’s use of the phrase ‘Chinese virus’ to refer to coronavirus, exploring people’s sentiments on Twitter becomes increasingly important to under- stand. This study explores how attitudes toward Asian people have changed across 10 different cities before, during, and after COVID-19 came to the U.S. on Twitter and attempts to understand if differences in attitudes can be attributed to racial demo- graphics of cities. We also perform a case study on attitudes expressed by members of Congress on Twitter towards Asian people during this time period, using VADER sentiment analysis and He et al.’s (2021) Asian hate classifier to perform this analysis. We utilize He et al.’s (2021) Asian hate classifier to classify tweets as counterspeech or hate. We then utilize USE embeddings and hierarchical agglomerative clustering to cluster tweets in each city and time period by semantic similarity to understand general topics of discussion. Finally, we utilize LDA topic modeling on the clusters for a more precise understanding of people’s attitudes towards Asians on Twitter. We found that Asian hate increased over the course of the pandemic, while coun- terspeech was at its highest during COVID-19’s height. People discussed anti-Asian and anti-China topics, but also discussed more neutral topics such as Asian Food or COVID-19 generally. Through this study, we hope to shed light on the landscape of Asian hate on Twitter and promote further analysis on this topic
AdonAI
No full textThis paper details the development of AdonAI, a web application designed to assist authors in overcoming writer’s block by offering automated plot and character suggestions in the form of tropes. AdonAI’s novel suggestion mechanism is powered by a plethora of natural language processing techniques in tandem with a digital humanities dataset that comprises preexisting stories and characters along with their pertinent tropes. This interactive dashboard uses the industry-standard three-tier architecture of user interface, processing, and data management to render chronological and presentational plot visualizations, Wikipedia-esque character biographies, and dynamic trope insights based on user input. This application and its corresponding written report serve as a springboard for future scholars to research the intersection between technology, religion, and storytelling, as well as a tool for authors to facilitate the brainstorming and outlining processes
Idiomatic sentence generation and paraphrasing
No full textIdiomatic expressions (IE) play an important role in natural language, and have long been a “pain in the neck” for NLP systems. Despite this, text generation tasks related to IEs remain largely under-explored. In this study, we propose two new tasks of idiomatic sentence generation and paraphrasing to fill this research gap. We introduce a curated dataset of 823 IEs, and a parallel corpus with sentences containing them and the same sentences where the IEs were replaced by their literal paraphrases as the primary resource for our tasks. We benchmark existing deep learning models, which have state-of-the-art performance on related tasks using automated and manual evaluation with our dataset to inspire further research on our proposed tasks. By establishing baseline models, we pave the way for more comprehensive and accurate modeling of IEs, both for generation and paraphrasing.
Inspired by psycholinguistic theories of idiom use in one’s native language, we also propose a novel approach for these tasks, which retrieves the appropriate idiom for a given literal sentence, extracts the span of the sentence to be replaced by the idiom, and generates the idiomatic sentence by using a large pre-trained language model to combine the retrieved idiom and the remainder of the sentence. For idiomatic sentence paraphrasing, the definition of the idiom in the given idiomatic sentence is first retrieved. Then the idiom in the sentence is extracted and finally, the literal counterpart is generated by a large pre-trained language model. Experiments on a novel dataset created for these tasks show that our model is able to work effectively. Furthermore, automatic and human evaluations show that for these tasks, the proposed model outperforms a series of competitive baseline models for text generation.
Being able to generate literal counterparts of high quality, our method for idiomatic sentence paraphrase is also used for constructing a larger corpus with the help of MAGPIE dataset. This enlarged corpus also helps to improve the performance of different models on idiomatic sentence generation.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo termsThe student, Jianing Zhou, accepted the attached license on 2021-04-21 at 13:35.The student, Jianing Zhou, submitted this Thesis for approval on 2021-04-21 at 13:40.This Thesis was approved for publication on 2021-04-23 at 16:05.DSpace SAF Submission Ingestion Package generated from Vireo submission #16472 on 2021-09-16 at 16:45:44Made available in DSpace on 2021-09-17T01:11:10Z (GMT). No. of bitstreams: 2
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Previous issue date: 2021-04-2
Geometry of compositionality
No full textWord embedding is a popular representation of words in vector space, and its geometry reveals the lexical semantics. This thesis further explores the interesting geometric properties of word embedding, and looks into its interaction with the context representation. We propose an innovative method to detect whether a given word or phrase is used literally in a specific context. This work focuses on three specific applications in natural language processing: idiomaticity, sarcasm and metaphor detection. Extensive experiments have shown that this embedding-based method achieves good performance in multiple languages.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-09-29 without embargo termsThe student, Hongyu Gong, accepted the attached license on 2017-07-10 at 15:27.The student, Hongyu Gong, submitted this Thesis for approval on 2017-07-10 at 15:59.This Thesis was approved for publication on 2017-07-11 at 16:26.DSpace SAF Submission Ingestion Package generated from Vireo submission #11378 on 2017-09-29 at 11:29:37Made available in DSpace on 2017-09-29T17:56:40Z (GMT). No. of bitstreams: 2
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Previous issue date: 2017-07-1
Automatic generation of tunable analogy benchmarks for word representations
No full textWe present a method to automatically generate syntactic analogy datasets for the evaluation of word representations in an unsupervised manner. The automatic generation also allows for customization in terms of word-frequencies, syntactic rules, part-of-speech tags and size of the dataset. We show the ability of our method to generate cross-lingual analogy task datasets for languages other than English, where evaluation datasets are limited if not nonexistent, by constructing datasets for French, German, Spanish, Arabic and Hebrew.
Our method clusters pairs of words into morphological rules in an unsupervised manner, using which we generate analogy questions for different rules. We show the quality of an automatically generated dataset by checking the correlation of the performance of different word representations on it with the performance of the same representations on the Google analogy dataset. The values exhibited a high correlation of 95%. Moreover, we showcase the benefits of customization through studying the performance of different word representations when varying the frequency of words in the dataset.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-11-09 without embargo termsThe student, Tarek Sakakini, accepted the attached license on 2016-07-16 at 17:49.The student, Tarek Sakakini, submitted this Thesis for approval on 2016-07-16 at 17:57.This Thesis was approved for publication on 2016-07-20 at 08:40.DSpace SAF Submission Ingestion Package generated from Vireo submission #9976 on 2016-11-09 at 10:25:18Made available in DSpace on 2016-11-10T17:55:16Z (GMT). No. of bitstreams: 2
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Previous issue date: 2016-07-2
An examination of gendered discourse in the discussion forums of online STEM courses
No full textWomen are underrepresented in science, technology, engineering, and mathematics (STEM) fields, a problem that has roots in their disproportional enrollment and retention in STEM courses at the collegiate level. Increasingly, introductory courses across the STEM disciplines are offered online. In this project, I focus on one potential gatekeeper to women’s online success: discussion forums.
Although many scholars agree that discussion forums are important components of online courses because of the collaboration and community they foster, there are gaps in our understanding of the mechanisms behind how discussion forums actually do that. One potential mechanism is language; studying the language of discussion forums can help us gain insight into students’ state of mind and propensity to form a community. By honing in on specific features of the discussion forums that have the potential to influence students’ interactions with one other (i.e., language), I can begin to develop concrete interventions to help students collaborate more effectively, develop community, and ultimately succeed in the course.
The first study of this dissertation describes the state of gendered language use in two online STEM courses. The second paper explores how that language interacts with one way of structuring a discussion forum to predict students’ final grades. That structure consisted of giving students the option to post a solution to a homework problem, ask a question, or answer someone’s question.
The results reveal that women and men did not differ in their language use along traditionally gendered lines, which is very promising for women in online courses; this means that it is possible that they can feel more comfortable because the language they use does not overtly mark them as a female, and therefore may subvert the typical result of the negative outcomes associated with that marker. Additionally, although not confined to one’s gender, elements of gendered discourse permeated the discussion forums. Gendered language was uniquely used among posting types and also was relevant to students’ final grades. Being a male, posting solutions, answering others’ questions, having larger word counts, as well as using more numbers and analytic language were all related to earning higher final grades.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-12-01The student, Genevieve Keyser, accepted the attached license on 2018-11-19 at 17:26.The student, Genevieve Keyser, submitted this Dissertation for approval on 2018-11-19 at 17:26.This Dissertation was approved for publication on 2018-11-26 at 11:56.DSpace SAF Submission Ingestion Package generated from Vireo submission #13104 on 2019-02-07 at 14:17:52Made available in DSpace on 2019-02-07T20:35:59Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-11-26Embargo set by: Seth Robbins for item 109825
Lift date: 2021-02-07T20:36:09Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 109825
Lift date: 2021-02-07T20:39:46Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 109825
Lift date: 2021-02-07T20:44:35Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 109825 on 2021-02-08T10:15:18Z
Striated pattern on scrotal ultrasonography: A marker for Non-hodgkins lymphoma of testis
No full textWe present a case of 52 year old man who presented with bilateral painless testicular swelling. On evaluation with high resolution ultrasonography of the scrotum, typical "striated pattern" which is highly suggestive of Non Hodgkins lymphoma of the testis was seen. The patient underwent bilateral inguinal orchidectomy and the histopathological examination confirmed the diagnosis of Non Hodgkins lymphoma
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