Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an impo
rtant empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.
In this paper we study pejorative language, an under-explored topic in computational linguistics. Unlike existing models of offensive language and hate speech, pejorative language manifests itself primarily at the lexical level, and describes a word
that is used with a negative connotation, making it different from offensive language or other more studied categories. Pejorativity is also context-dependent: the same word can be used with or without pejorative connotations, thus pejorativity detection is essentially a problem similar to word sense disambiguation. We leverage online dictionaries to build a multilingual lexicon of pejorative terms for English, Spanish, Italian, and Romanian. We additionally release a dataset of tweets annotated for pejorative use. Based on these resources, we present an analysis of the usage and occurrence of pejorative words in social media, and present an attempt to automatically disambiguate pejorative usage in our dataset.
The need to deploy large-scale pre-trained models on edge devices under limited computational resources has led to substantial research to compress these large models. However, less attention has been given to compress the task-specific models. In th
is work, we investigate the different methods of unstructured pruning on task-specific models for Aspect-based Sentiment Analysis (ABSA) tasks. Specifically, we analyze differences in the learning dynamics of pruned models by using the standard pruning techniques to achieve high-performing sparse networks. We develop a hypothesis to demonstrate the effectiveness of local pruning over global pruning considering a simple CNN model. Later, we utilize the hypothesis to demonstrate the efficacy of the pruned state-of-the-art model compared to the over-parameterized state-of-the-art model under two settings, the first considering the baselines for the same task used for generating the hypothesis, i.e., aspect extraction and the second considering a different task, i.e., sentiment analysis. We also provide discussion related to the generalization of the pruning hypothesis.
Finding the year of writing for a historical text is of crucial importance to historical research. However, the year of original creation is rarely explicitly stated and must be inferred from the text content, historical records, and codicological cl
ues. Given a transcribed text, machine learning has successfully been used to estimate the year of production. In this paper, we present an overview of several estimation approaches for historical text archives spanning from the 12th century until today.
This paper describes the primarily-graduate computational linguistics and NLP curriculum at Georgetown University, a U.S. university that has seen significant growth in these areas in recent years. We reflect on the principles behind our curriculum c
hoices, including recognizing the various academic backgrounds and goals of our students; teaching a variety of skills with an emphasis on working directly with data; encouraging collaboration and interdisciplinary work; and including languages beyond English. We reflect on challenges we have encountered, such as the difficulty of teaching programming skills alongside NLP fundamentals, and discuss areas for future growth.
We provide an overview of a new Computational Text Analysis course that will be taught at Barnard College over a six week period in May and June 2021. The course is targeted to non Computer Science at a Liberal Arts college that wish to incorporate f
undamental Natural Language Processing tools in their re- search and studies. During the course, students will complete daily programming tutorials, read and review contemporary research papers, and propose and develop independent research projects.
This report describes the course Evaluation of NLP Systems, taught for Computational Linguistics undergraduate students during the winter semester 20/21 at the University of Potsdam, Germany. It was a discussion-based seminar that covered different a
spects of evaluation in NLP, namely paradigms, common procedures, data annotation, metrics and measurements, statistical significance testing, best practices and common approaches in specific NLP tasks and applications.
Abstract Slang is a common type of informal language, but its flexible nature and paucity of data resources present challenges for existing natural language systems. We take an initial step toward machine generation of slang by developing a framework
that models the speaker's word choice in slang context. Our framework encodes novel slang meaning by relating the conventional and slang senses of a word while incorporating syntactic and contextual knowledge in slang usage. We construct the framework using a combination of probabilistic inference and neural contrastive learning. We perform rigorous evaluations on three slang dictionaries and show that our approach not only outperforms state-of-the-art language models, but also better predicts the historical emergence of slang word usages from 1960s to 2000s. We interpret the proposed models and find that the contrastively learned semantic space is sensitive to the similarities between slang and conventional senses of words. Our work creates opportunities for the automated generation and interpretation of informal language.
In this study, we present a state-of-art model; we call SYRIA, to simulate the activity of ventricular myocardial cell as an example of simulating a human cell, in which we use the latest mathematical models of cardiac cell. We rely on O'Hara (O'Hara
, et al., 2011) for modeling electrical activity, ions hemostasis, and contracting. Our presented model takes into consideration the role of potassium channels KATP, chloride channels, volume regulation channels based on the Kyoto model (A.Takeuchi, 2006), PH regulation channels based on Leem model (Leem, et al., 1999), and the improvement of the values of some variables based on the results of modern experiments, especially concentrations of ions within the mitochondrial and cytoplasm, the values of calcium buffers in the SR, values of the conductance of membrane channels, and concentrations of metabolites in the mitochondria. The previous models have been linked to a mitochondrial model based on Kembro (Kembro, et al., 2013). The SYRIA model is based on the integration and improvement of the best known models in a hierarchical structure that facilitates understanding, monitoring and reuse, we also present models for testing drugs and some external influences. The programming process is done using blocks of M-file and S-function in Simulink.
By comparing the results obtained from the simulation with the laboratory results, we observe that computer simulations give results within the normal physiological range
.
The Electromagnetic Interference and EMC are one important phenomenon, since the EMI
causes degradation in the performance of electric and electronic instruments.
The EMI- problem may decrease effectiveness of sensitive devices and even may lead to
a
failure of its operation.
This paper studies EMI-problem between different systems by using convenient computer
programs as CST, which provides modulation and simulation of this problem. This method provide
ability to trace and evaluate EMI microscopically in space and real time.
