The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations

The shared task of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) aims at providing a common benchmark for testing current corpus-based methods for the identifica- tion of lexical semantic relations ( synonymy , antonymy , hypernymy , part-whole meronymy ) and at gaining a better understanding of their respective strengths and weaknesses. The shared task uses a challenging dataset extracted from EVALution 1.0 (Santus et al., 2015b), which contains word pairs holding the above-mentioned relations as well as semantically unrelated control items ( random ). The task is split into two subtasks: (i) identification of related word pairs vs. unre- lated ones; (ii) classification of the word pairs according to their semantic relation. This paper describes the subtasks, the dataset, the evaluation metrics, the seven participating systems and their results. The best performing system in subtask 1 is GHHH ( F 1 = 0 . 790 ), while the best system in subtask 2 is LexNet ( F 1 = 0 . 445 ). The dataset and the task description are available at https://sites.google.com/site/cogalex2016/home/shared-task

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.Comment: 24 Pages, 8 Tables, 6 Figures; Accepted by PLoS One ; One of the two mentioned Datasets in the manuscript has Closed Access. We will make it public after PLoS One produces the manuscrip

Enhancing the Reasoning Capabilities of Natural Language Inference Models with Attention Mechanisms and External Knowledge

Natural Language Inference (NLI) is fundamental to natural language understanding. The task summarises the natural language understanding capabilities within a simple formulation of determining whether a natural language hypothesis can be inferred from a given natural language premise. NLI requires an inference system to address the full complexity of linguistic as well as real-world commonsense knowledge and, hence, the inferencing and reasoning capabilities of an NLI system are utilised in other complex language applications such as summarisation and machine comprehension. Consequently, NLI has received significant recent attention from both academia and industry. Despite extensive research, contemporary neural NLI models face challenges arising from the sole reliance on training data to comprehend all the linguistic and real-world commonsense knowledge. Further, different attention mechanisms, crucial to the success of neural NLI models, present the prospects of better utilisation when employed in combination. In addition, the NLI research field lacks a coherent set of guidelines for the application of one of the most crucial regularisation hyper-parameters in the RNN-based NLI models -- dropout. In this thesis, we present neural models capable of leveraging the attention mechanisms and the models that utilise external knowledge to reason about inference. First, a combined attention model to leverage different attention mechanisms is proposed. Experimentation demonstrates that the proposed model is capable of better modelling the semantics of long and complex sentences. Second, to address the limitation of the sole reliance on the training data, two novel neural frameworks utilising real-world commonsense and domain-specific external knowledge are introduced. Employing the rule-based external knowledge retrieval from the knowledge graphs, the first model takes advantage of the convolutional encoders and factorised bilinear pooling to augment the reasoning capabilities of the state-of-the-art NLI models. Utilising the significant advances in the research of contextual word representations, the second model, addresses the existing crucial challenges of external knowledge retrieval, learning the encoding of the retrieved knowledge and the fusion of the learned encodings to the NLI representations, in unique ways. Experimentation demonstrates the efficacy and superiority of the proposed models over previous state-of-the-art approaches. Third, for the limitation on dropout investigations, formulated on exhaustive evaluation, analysis and validation on the proposed RNN-based NLI models, a coherent set of guidelines is introduced

Exposing implicit biases and stereotypes in human and artificial intelligence: state of the art and challenges with a focus on gender

Biases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes serve a multifarious set of cognitive goals, while at the same time stressing their potential harmfulness. Recently, biases and stereotypes became the purview of heated debates in the machine learning community too. Researchers and developers are becoming increasingly aware of the fact that some biases, like gender and race biases, are entrenched in the algorithms some AI applications rely upon. Here, taking into account several existing approaches that address the problem of implicit biases and stereotypes, we propose that a strategy to cope with this phenomenon is to unmask those found in AI systems by understanding their cognitive dimension, rather than simply trying to correct algorithms. To this extent, we present a discussion bridging together findings from cognitive science and insights from machine learning that can be integrated in a state-of-the-art semantic network. Remarkably, this resource can be of assistance to scholars (e.g., cognitive and computer scientists) while at the same time contributing to refine AI regulations affecting social life. We show how only through a thorough understanding of the cognitive processes leading to biases, and through an interdisciplinary effort, we can make the best of AI technology

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail