Buyer intent in e-commerce is multi-faceted and is expressed through explicit attributes—such as brand, size, color, and material, rather than through general topical relevance. However, many state-of-the-art scalable query-product matching systems rely on aggregate representations, scoring a single query embedding against a single item embedding. While efficient, this aggregation frequently fails to satisfy individual attribute intent: items can be semantically related, yet violate key aspects specified in the query. In contrast, fine-grained interaction methods can better capture aspect-level constraints, but are typically too expensive due to increased run-time computation and storage costs. We propose an aspect-aware ranking framework that retrieves and resolves aspects in queries and performs fine-grained semantic affinity match against aspects in products to compute an aggregate query-product level aspect affinity score. The proposed approach integrates (i) query aspect resolution (canonicalization) using structured aspect data, (ii) a model to learn granular aspect affinity signal capturing individual aspect-level understanding; and iii) an efficient design for online serving, significantly cutting cost associated with inference speed and storage. This design preserves the scalability of two-tower retrieval while substantially improving explicit intent satisfaction.

As part of the large number of scientific articles being published every year, the publication rate of biomedical literature has been increasing. Consequently, there has been considerable effort to harness and summarize the massive amount of biomedical research articles. While transformer-based encoder-decoder models in a vanilla source document-to-summary setting have been extensively studied for abstractive summarization in different domains, their major limitations continue to be entity hallucination (a phenomenon where generated summaries constitute entities not related to or present in source article(s)) and factual inconsistency. This problem is exacerbated in a biomedical setting where named entities and their semantics (which can be captured through a knowledge base) constitute the essence of an article. The use of named entities and facts mined from background knowledge bases pertaining to the named entities to guide abstractive summarization has not been studied in biomedical article summarization literature. In this paper, we propose an entity-driven fact-aware framework for training end-to-end transformer-based encoder-decoder models for abstractive summarization of biomedical articles. We call the proposed approach, whose building block is a transformer-based model, EFAS, Entity-driven Fact-aware Abstractive Summarization. We conduct a set of experiments using five state-of-the-art transformer-based encoder-decoder models (two of which are specifically designed for long document summarization) and demonstrate that injecting knowledge into the training/inference phase of these models enables the models to achieve significantly better performance than the standard source document-to-summary setting in terms of entity-level factual accuracy, N-gram novelty, and semantic equivalence while performing comparably on ROUGE metrics. The proposed approach is evaluated on ICD-11-Summ-1000, a dataset we build for abstractive summarization of biomedical literature, and PubMed-50k, a segment of a large-scale benchmark dataset for abstractive summarization of biomedical literature.

Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or groups of documents, however, has remained elusive, with many efforts limited to extraction of keywords, key phrases, or key sentences. Accurate abstractive summarization has yet to be achieved due to the inherent difficulty of the problem, and limited availability of training data. In this paper, we propose a topic-centric unsupervised multi-document summarization framework to generate extractive and abstractive summaries for groups of scientific articles across 20 Fields of Study (FoS) in Microsoft Academic Graph (MAG) and news articles from DUC-2004 Task 2. The proposed algorithm generates an abstractive summary by developing salient language unit selection and text generation techniques. Our approach matches the state-of-the-art when evaluated on automated extractive evaluation metrics and performs better for abstractive summarization on five human evaluation metrics (entailment, coherence, conciseness, readability, and grammar). We achieve a kappa score of 0.68 between two co-author linguists who evaluated our results. We plan to publicly share MAG- 20, a human-validated gold standard dataset of topic-clustered research articles and their summaries to promote research in abstractive summarization.

Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (SAMHSA) shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US. Prior research involving surveys and questionnaires (e.g. PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We also use language modeling, medical entity recognition and normalization and negation detection to create a dataset of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in Columbia Suicide Severity Rating Scale (C-SSRS), with the pairwise annotator agreement of 0.79 and group-wise agreement of 0.73. Compared to the existing four-label classification scheme (no risk, low risk, moderate risk, and high risk), our proposed C-SSRS-based 5-label classification scheme distinguishes people who are supportive, from those who show different severity of suicidal tendency. Our 5-label classification scheme outperforms the state-of-the-art schemes by improving the graded recall by 4.2% and reducing the perceived risk measure by 12.5%. Convolutional neural network (CNN) provided the best performance in our scheme due to the discriminative features and use of domain-specific knowledge resources, in comparison to SVM-L that has been used in the state-of-the-art tools over similar dataset.

Terror attacks have been linked in part to online extremist content. Online conversations are cloaked in religious ambiguity, with deceptive intentions, often twisted from mainstream meaning to serve a malevolent ideology. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Radicalization is a subtle long-running persuasive process that occurs over time. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. Based on prior empirical and qualitative research in social sciences, particularly political science, we model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. The significant sensitivity of the Islamist extremist ideology and its local and global security implications require reliable algorithms for modelling such communications on Twitter. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate, which reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our context-aware approach outperforms a competitive baseline by 10.2% in precision, thereby enhancing the potential of such tools for use in human review.

Social media platforms are increasingly being used to share and seek advice on mental health issues. In particular, Reddit users freely discuss such issues on various subreddits, whose structure and content can be leveraged to formally interpret and relate subreddits and their posts in terms of mental health diagnostic categories. There is prior research on the extraction of mental health-related information, including symptoms, diagnosis, and treatments from social media; however, our approach can additionally provide actionable information to clinicians about the mental health of a patient in diagnostic terms for web-based intervention. Specifically, we provide a detailed analysis of the nature of subreddit content from domain expert's perspective and introduce a novel approach to map each subreddit to the best matching DSM-5 (Diagnostic and Statistical Manual of Mental Disorders - 5th Edition) category using multi-class classifier. Our classification algorithm analyzes all the posts of a subreddit by adapting topic modeling and word-embedding techniques, and utilizing curated medical knowledge bases to quantify relationship to DSM-5 categories. Our semantic encoding-decoding optimization approach reduces the false-alarm-rate from 30% to 2.5% over a comparable heuristic baseline, and our mapping results have been verified by domain experts achieving a kappa score of 0.84.