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Articles 1 - 6 of 6
Full-Text Articles in Computational Linguistics
Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat
Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat
Graduate Theses/Dissertations
Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …
Is Neuro-Symbolic Ai Meeting Its Promises In Natural Language Processing? A Structured Review, Kyle Hamilton, Kyle Hamilton, Aparna Nayak, Bojan Bozic, Luca Longo
Is Neuro-Symbolic Ai Meeting Its Promises In Natural Language Processing? A Structured Review, Kyle Hamilton, Kyle Hamilton, Aparna Nayak, Bojan Bozic, Luca Longo
Articles
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its …
From Sesame Street To Beyond: Multi-Domain Discourse Relation Classification With Pretrained Bert, Isaac R. Raff
From Sesame Street To Beyond: Multi-Domain Discourse Relation Classification With Pretrained Bert, Isaac R. Raff
Dissertations, Theses, and Capstone Projects
Research efforts in transfer learning have gained massive popularity in recent years. Pretrained language models have demonstrated the most successful results in producing high quality neural networks capable of quality inference after training across domains via transfer learning. This study expands on the domain transfer introduced in \cite{ferracane-etal-2019-news} exploring neural methods for transfer learning of discourse parsing between a news source domain and a medical target domain. \cite{ferracane-etal-2019-news} specifically discuss transfer learning from news articles to PubMed medical journal articles. Experiments in transfer learning in the current work expand to include three domains: Wall Street Journal articles previously annotated with …
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Northeast Journal of Complex Systems (NEJCS)
In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …
Label Imputation For Homograph Disambiguation: Theoretical And Practical Approaches, Jennifer M. Seale
Label Imputation For Homograph Disambiguation: Theoretical And Practical Approaches, Jennifer M. Seale
Dissertations, Theses, and Capstone Projects
This dissertation presents the first implementation of label imputation for the task of homograph disambiguation using 1) transcribed audio, and 2) parallel, or translated, corpora. For label imputation from parallel corpora, a hypothesis of interlingual alignment between homograph pronunciations and text word forms is developed and formalized. Both audio and parallel corpora label imputation techniques are tested empirically in experiments that compare homograph disambiguation model performance using: 1) hand-labeled training data, and 2) hand-labeled training data augmented with label-imputed data. Regularized, multinomial logistic regression and pre-trained ALBERT, BERT, and XLNet language models fine-tuned as token classifiers are developed for homograph …
Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher
Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher
Conference papers
In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.