Taher Rahgooy

University of West Florida | UWF · Department of Computer Science

Modeling human decision making plays a fundamental role in the design of intelligent systems capable of rich interactions and effective teamwork. In this paper we consider the task of choice prediction in settings with multiple alternatives. Cognitive models of decision making can successfully replicate and explain behavioral effects involving unce...

Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios force us to evaluate the trade-off between collective rules and norms with our own personal objectives and desires. To create effective AI-human teams, we must equip AI age...

Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these...

The intended sarcasm cannot be understood until the listener observes that the text’s literal meaning violates truthfulness. Consequently, words and meanings play an essential role in specifying sarcasm. Enriched feature extraction techniques were proposed to capture both words and meanings in the contexts. Due to the overlapping features in sarcas...

Figurative language is using words in a way that deviates from the conventional order and meaning in order to ask the reader or listener to understand the meaning by virtue of its relation to some other meaning or concept. It is a rapidly growing area in Natural Language Processing, including the processing of irony, sarcasm, as well as other figur...

Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios force us to evaluate the trade-off between collective norms and our own personal objectives. To create effective AI-human teams, we must equip AI agents with a model of how...

Hate Speech (HS) in social media such as Twitter is a complex phenomenon that attracted a significant body of research in the NLP. HS Spreaders (haters) aim to spread HS via social media. In this task, we aim to identify such haters. On one hand, our proposed class-dependent LDSE representation is fed to a linear SVM classifier to identify the hate...

Toxic Spans Detection(TSD) task is defined as highlighting spans that make a text toxic. Many works have been done to classify a given comment or document as toxic or non-toxic. However, none of those proposed models work at the token level. In this paper, we propose a self-attention-based bidirectional gated recurrent unit(BiGRU) with a multi-embe...

Author Attribution (AA) as one of the most important tasks of authorship analysis attracted huge body of research in recent years. In this task, given a document, the goal is to identify its author from a set of known authors and samples of their writings. In PAN 2019 shared tasks, the AA task is expanded in two ways. First, by having documents wri...

Author verification algorithms mainly rely on learning statistical fingerprints of authors. In the other hand, most of the previous algorithms in author masking try to apply changes to the original texts blindly without considering those fingerprints. In this paper, we propose an approach that learns author's fingerprints and uses them to apply dir...

Understanding human decision processes has been a topic of intense study in different disciplines including psychology, economics, and artificial intelligence. Indeed, modeling human decision making plays a fundamental role in the design of intelligent systems capable of rich interactions. Decision Field Theory (DFT) [3] provides a cognitive model...

Author Profiling is one of the most important tasks in authorship analysis. In PAN 2019 shared tasks, the gender identification of the author is the main focus. Compared to the previous year the author profiling task is expended by having documents written by bots. In order to tackle this new challenge we propose a two phase approach. In the first...

The extraction of spatial semantics is important in many real-world applications such as geographical information systems, robotics and navigation, semantic search, etc. Moreover, spatial semantics are the most relevant semantics related to the visualization of language. The goal of multimodal spatial role labeling task is to extract spatial inform...

This work is on a previously formalized semantic evaluation task of spatial role labeling (SpRL) that aims at extraction of formal spatial meaning from text. Here, we report the results of initial efforts towards exploiting visual information in the form of images to help spatial language understanding. We discuss the way of designing new models in...

The extraction of spatial semantics is important in many real-world applications such as geographical information systems, robotics and navigation, semantic search, etc. Moreover, spatial semantics are the most relevant semantics related to the visualization of language. The goal of multimodal spatial role labeling task is to extract spatial inform...

As almost all the successful author identification approaches are based on the word frequencies, the most obvious way to obfuscate a text is to distort those frequencies. In this paper we chose a subset of the most frequent words for an author and replace each one with one of their synonyms. In order to select the best synonym, we considered two me...

The Author Identification task for PAN 2016 consisted of three different Sub-tasks: authorship clustering, authorship links and author diarization. We developed a machine learning approaches for two of three of these tasks. For the two authorship related tasks we created various sets of feature spaces. The challenge was to combine these feature spa...

In this paper, application of fuzzy system of linear equations (FSLE) is investigated in the circuit analysis (CA). In the field of CA, each circuit includes linear resistance, inductance and capacitance are modeled to complex linear equation. A fuzzy current or voltage in the circuit is more intellectual relative to crisp value because of some rea...