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Register a new userSuicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning
كشف الانتحار عن طريق تمثيلات المستخدم الاجتماعية والزمنية باستخدام التعلم القطعي
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تشير الدراسات النفسية الأخيرة إلى أن الأفراد الذين يعرضون التفكير الانتحاري يتحول بشكل متزايد إلى وسائل التواصل الاجتماعي بدلا من ممارسي الصحة العقلية.شخصيا سياقته في تراكم هذا الاضطراب أمر بالغ الأهمية لتحديد دقيق للمستخدمين المعرضين للخطر.في هذا العمل، نقترح إطارا يشترك في الاستفادة من التاريخ العاطفي للمستخدم والمعلومات الاجتماعية من حي المستخدم في شبكة إلى السياق تفسير أحدث تغريد المستخدم على Twitter.تعكس الطبيعة الخالية من النطاق لعلاقات الشبكة الاجتماعية، نقترح استخدام شبكات استئصال الرسم البياني القطعي، والتركيبة مع عملية الصقور لتعلم الطيف العاطفي التاريخي للمستخدم بطريقة حساسة للوقت.يتفوق نظامنا بشكل كبير على الأساليب الحديثة في هذه المهمة، مما يظهر فوائد كل من تمثيلات السياق الاجتماعي والخاصة.
Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly leveraging a user's emotional history and social information from a user's neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, in combination with the Hawkes process to learn the historical emotional spectrum of a user in a time-sensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.
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Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to
detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces.
Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works attempt to automatically expand existing taxonomies to improve their cover
age by learning concept embeddings in Euclidean space, while taxonomies, inherently hierarchical, more naturally align with the geometric properties of a hyperbolic space. In this paper, we present HyperExpan, a taxonomy expansion algorithm that seeks to preserve the structure of a taxonomy in a more expressive hyperbolic embedding space and learn to represent concepts and their relations with a Hyperbolic Graph Neural Network (HGNN). Specifically, HyperExpan leverages position embeddings to exploit the structure of the existing taxonomies, and characterizes the concept profile information to support the inference on new concepts that are unseen during training. Experiments show that our proposed HyperExpan outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks.
Many existing chatbots do not effectively support mixed initiative, forcing their users to either respond passively or lead constantly. We seek to improve this experience by introducing new mechanisms to encourage user initiative in social chatbot co
nversations. Since user initiative in this setting is distinct from initiative in human-human or task-oriented dialogue, we first propose a new definition that accounts for the unique behaviors users take in this context. Drawing from linguistics, we propose three mechanisms to promote user initiative: back-channeling, personal disclosure, and replacing questions with statements. We show that simple automatic metrics of utterance length, number of noun phrases, and diversity of user responses correlate with human judgement of initiative. Finally, we use these metrics to suggest that these strategies do result in statistically significant increases in user initiative, where frequent, but not excessive, back-channeling is the most effective strategy.
Background:
Because of the obvious increasing in the poisoning cases in the
recent decades . The aim of the study was to determine the
medicine groups that were used to commit suicide and suicide
attempts.
Methods:
Our study contained two sampl
es . . The first was 175 suicide
attempts ( 50 males, 125 females ), which were archives in the
Syrian poison information center in the period between 1998-1999.
These cases were divided into sex, age groups, incidence date The second was 84 suicide cases ( 54 males, 30 females ), which were archives in the medicolegal center in Damascus during the period between 1996 – 2000 . Out of 12 cases were because of using medicines, and the medicines classified according to the therapeutic medicinal groups.
Results:
Analgesics were used in 25.36% of all suicide attempts.
Paracetamol formed 94% out of it. The psychiatric tablets were
used 20.65%. The analgesics were used in 33.33% of all suicide cases. In one third of cases the persons used their own medicines.
The incidence of suicide attempts by using medicines for male:
female was 1:2.5. 48% of all suicide attempts were in the group 10- 20 years and 32% were in summer.
Conclusion: Most of suicide attempts by using medicines were due to paracetamol. So, we need other studies about suicide attempts in another cities .
Large-scale auto-regressive models have achieved great success in dialogue response generation, with the help of Transformer layers. However, these models do not learn a representative latent space of the sentence distribution, making it hard to cont
rol the generation. Recent works have tried on learning sentence representations using Transformer-based framework, but do not model the context-response relationship embedded in the dialogue datasets. In this work, we aim to construct a robust sentence representation learning model, that is specifically designed for dialogue response generation, with Transformer-based encoder-decoder structure. An utterance-level contrastive learning is proposed, encoding predictive information in each context representation for its corresponding response. Extensive experiments are conducted to verify the robustness of the proposed representation learning mechanism. By using both reference-based and reference-free evaluation metrics, we provide detailed analysis on the generated sentences, demonstrating the effectiveness of our proposed model.
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