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Computational Linguistics Commons

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Full-Text Articles in Computational Linguistics

Ideology Prediction From Scarce And Biased Supervision: Learn To Disregard The “What” And Focus On The “How”!, Chen Chen, Dylan Walker, Venkatesh Saligrama Jul 2023

Ideology Prediction From Scarce And Biased Supervision: Learn To Disregard The “What” And Focus On The “How”!, Chen Chen, Dylan Walker, Venkatesh Saligrama

Business Faculty Articles and Research

We propose a novel supervised learning approach for political ideology prediction (PIP) that is capable of predicting out-of-distribution inputs. This problem is motivated by the fact that manual data-labeling is expensive, while self-reported labels are often scarce and exhibit significant selection bias. We propose a novel statistical model that decomposes the document embeddings into a linear superposition of two vectors; a latent neutral context vector independent of ideology, and a latent position vector aligned with ideology. We train an end-to-end model that has intermediate contextual and positional vectors as outputs. At deployment time, our model predicts labels for input documents …


Improving Sign Recognition With Phonology, Lee Kezar, Jesse Thomason, Zed Sevcikova Sehyr May 2023

Improving Sign Recognition With Phonology, Lee Kezar, Jesse Thomason, Zed Sevcikova Sehyr

Communication Sciences and Disorders Faculty Articles and Research

We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language understanding. Our key insight is to explicitly recognize the role of phonology in sign production to achieve more accurate ISLR than existing work which does not consider sign language phonology. We train ISLR models that take in pose estimations of a signer producing a single sign to predict not only the sign but additionally its phonological characteristics, such as the handshape. These auxiliary predictions lead to a nearly 9% absolute gain in sign recognition …


An Interactive Visual Database For American Sign Language Reveals How Signs Are Organized In The Mind, Zed Sevcikova Sehyr, Ariel Goldberg, Karen Emmory, Naomi Caselli Apr 2021

An Interactive Visual Database For American Sign Language Reveals How Signs Are Organized In The Mind, Zed Sevcikova Sehyr, Ariel Goldberg, Karen Emmory, Naomi Caselli

Communication Sciences and Disorders Faculty Articles and Research

"We are four researchers who study psycholinguistics, linguistics, neuroscience and deaf education. Our team of deaf and hearing scientists worked with a group of software engineers to create the ASL-LEX database that anyone can use for free. We cataloged information on nearly 3,000 signs and built a visual, searchable and interactive database that allows scientists and linguists to work with ASL in entirely new ways."


The Asl-Lex 2.0 Project: A Database Of Lexical And Phonological Properties For 2,723 Signs In American Sign Language, Zed Sevcikova Sehyr, Naomi Caselli, Ariel M. Cohen-Goldberg, Karen Emmory Feb 2021

The Asl-Lex 2.0 Project: A Database Of Lexical And Phonological Properties For 2,723 Signs In American Sign Language, Zed Sevcikova Sehyr, Naomi Caselli, Ariel M. Cohen-Goldberg, Karen Emmory

Communication Sciences and Disorders Faculty Articles and Research

ASL-LEX is a publicly available, large-scale lexical database for American Sign Language (ASL). We report on the expanded database (ASL-LEX 2.0) that contains 2,723 ASL signs. For each sign, ASL-LEX now includes a more detailed phonological description, phonological density and complexity measures, frequency ratings (from deaf signers), iconicity ratings (from hearing non-signers and deaf signers), transparency (“guessability”) ratings (from non-signers), sign and videoclip durations, lexical class, and more. We document the steps used to create ASL-LEX 2.0 and describe the distributional characteristics for sign properties across the lexicon and examine the relationships among lexical and phonological properties of signs. Correlation …


Chaprates, Brinly Xavier, Micole Amanda Marietta, Nidhi Vedantam May 2020

Chaprates, Brinly Xavier, Micole Amanda Marietta, Nidhi Vedantam

Student Scholar Symposium Abstracts and Posters

On the Chapman campus, through taking and choosing various classes, there is a significant need for communication and feedback between students and peers, professors, tutors, and study groups. With this, we wanted to create an application that enables users from various majors to not only easily and effectively communicate with various people in their field, but one that also enables them to give and receive feedback on various classes through a rating system. We believe that the application will aid students in a myriad of specific ways, including being involved in study groups and getting tutoring help, determining which classes …


Lexicalization And De-Lexicalization Processes In Sign Languages: Comparing Depicting Constructions And Viewpoint Gestures, Kearsy Cormier, David Quinto-Pozos, Zed Sehyr, Adam Schembri Nov 2012

Lexicalization And De-Lexicalization Processes In Sign Languages: Comparing Depicting Constructions And Viewpoint Gestures, Kearsy Cormier, David Quinto-Pozos, Zed Sehyr, Adam Schembri

Communication Sciences and Disorders Faculty Articles and Research

In this paper, we compare so-called “classifier” constructions in signed languages (which we refer to as “depicting constructions”) with comparable iconic gestures produced by non-signers. We show clear correspondences between entity constructions and observer viewpoint gestures on the one hand, and handling constructions and character viewpoint gestures on the other. Such correspondences help account for both lexicalisation and de-lexicalisation processes in signed languages and how these processes are influenced by viewpoint. Understanding these processes is crucial when coding and annotating natural sign language data.