47 research outputs found
Depression and Self-Harm Risk Assessment in Online Forums
Users suffering from mental health conditions often turn to online resources
for support, including specialized online support communities or general
communities such as Twitter and Reddit. In this work, we present a neural
framework for supporting and studying users in both types of communities. We
propose methods for identifying posts in support communities that may indicate
a risk of self-harm, and demonstrate that our approach outperforms strong
previously proposed methods for identifying such posts. Self-harm is closely
related to depression, which makes identifying depressed users on general
forums a crucial related task. We introduce a large-scale general forum dataset
("RSDD") consisting of users with self-reported depression diagnoses matched
with control users. We show how our method can be applied to effectively
identify depressed users from their use of language alone. We demonstrate that
our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4,
FastText baseline, and CNN-
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge
Citation texts are sometimes not very informative or in some cases inaccurate
by themselves; they need the appropriate context from the referenced paper to
reflect its exact contributions. To address this problem, we propose an
unsupervised model that uses distributed representation of words as well as
domain knowledge to extract the appropriate context from the reference paper.
Evaluation results show the effectiveness of our model by significantly
outperforming the state-of-the-art. We furthermore demonstrate how an effective
contextualization method results in improving citation-based summarization of
the scientific articles.Comment: SIGIR 201
Triaging Content Severity in Online Mental Health Forums
Mental health forums are online communities where people express their issues
and seek help from moderators and other users. In such forums, there are often
posts with severe content indicating that the user is in acute distress and
there is a risk of attempted self-harm. Moderators need to respond to these
severe posts in a timely manner to prevent potential self-harm. However, the
large volume of daily posted content makes it difficult for the moderators to
locate and respond to these critical posts. We present a framework for triaging
user content into four severity categories which are defined based on
indications of self-harm ideation. Our models are based on a feature-rich
classification framework which includes lexical, psycholinguistic, contextual
and topic modeling features. Our approaches improve the state of the art in
triaging the content severity in mental health forums by large margins (up to
17% improvement over the F-1 scores). Using the proposed model, we analyze the
mental state of users and we show that overall, long-term users of the forum
demonstrate a decreased severity of risk over time. Our analysis on the
interaction of the moderators with the users further indicates that without an
automatic way to identify critical content, it is indeed challenging for the
moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for
Information Science and Technology (2017
CEDR: Contextualized Embeddings for Document Ranking
Although considerable attention has been given to neural ranking
architectures recently, far less attention has been paid to the term
representations that are used as input to these models. In this work, we
investigate how two pretrained contextualized language models (ELMo and BERT)
can be utilized for ad-hoc document ranking. Through experiments on TREC
benchmarks, we find that several existing neural ranking architectures can
benefit from the additional context provided by contextualized language models.
Furthermore, we propose a joint approach that incorporates BERT's
classification vector into existing neural models and show that it outperforms
state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR
(Contextualized Embeddings for Document Ranking). We also address practical
challenges in using these models for ranking, including the maximum input
length imposed by BERT and runtime performance impacts of contextualized
language models.Comment: Appeared in SIGIR 2019, 4 page