Topic-time Heatmaps for Human-in-the-loop Topic Detection and Tracking

• URL: http://arxiv.org/abs/2110.07337v1
• Date: Tue, 12 Oct 2021 19:17:56 GMT
• Title: Topic-time Heatmaps for Human-in-the-loop Topic Detection and Tracking
• Authors: Doug Beeferman, Hang Jiang
• Abstract summary: Topic Detection and Tracking (TDT) aims to organize a collection of news media into clusters of stories that pertain to the same real-world event. To apply TDT models to practical applications such as search engines and discovery tools, human guidance is needed to pin down the scope of an "event" for the corpus of interest. We generate a visual overview of the entire corpus, allowing the user to select regions of interest from the overview, and then ask a series of questions to affirm (or reject) that the selected documents belong to the same event.
• Score: 3.7057859167913456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The essential task of Topic Detection and Tracking (TDT) is to organize a collection of news media into clusters of stories that pertain to the same real-world event. To apply TDT models to practical applications such as search engines and discovery tools, human guidance is needed to pin down the scope of an "event" for the corpus of interest. In this work in progress, we explore a human-in-the-loop method that helps users iteratively fine-tune TDT algorithms so that both the algorithms and the users themselves better understand the nature of the events. We generate a visual overview of the entire corpus, allowing the user to select regions of interest from the overview, and then ask a series of questions to affirm (or reject) that the selected documents belong to the same event. The answers to these questions supplement the training data for the event similarity model that underlies the system.

Related papers

• Spatio-Temporal Context Prompting for Zero-Shot Action Detection [13.22912547389941]
  We propose a method which can effectively leverage the rich knowledge of visual-language models to perform Person-Context Interaction. To address the challenge of recognizing distinct actions by multiple people at the same timestamp, we design the Interest Token Spotting mechanism. Our method achieves superior results compared to previous approaches and can be further extended to multi-action videos.
  arXiv  Detail & Related papers  (2024-08-28T17:59:05Z)
• Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search [2.633652471066059]
  ISI-Clear is a state-of-the-art, cross-lingual, zero-shot event extraction system. It makes global events available on-demand, processing user-supplied text in 100 languages.
  arXiv  Detail & Related papers  (2023-05-17T20:41:51Z)
• Zero-Shot On-the-Fly Event Schema Induction [61.91468909200566]
  We present a new approach in which large language models are utilized to generate source documents that allow predicting, given a high-level event definition, the specific events, arguments, and relations between them. Using our model, complete schemas on any topic can be generated on-the-fly without any manual data collection, i.e., in a zero-shot manner.
  arXiv  Detail & Related papers  (2022-10-12T14:37:00Z)
• eCDT: Event Clustering for Simultaneous Feature Detection and Tracking- [4.094848360328624]
  Event Clustering-based Detection Tracking (eCDT) Our method 30% better feature tracking ages compared with the state-of-the-art approach.
  arXiv  Detail & Related papers  (2022-07-19T07:39:04Z)
• Unifying Event Detection and Captioning as Sequence Generation via Pre-Training [53.613265415703815]
  We propose a unified pre-training and fine-tuning framework to enhance the inter-task association between event detection and captioning. Our model outperforms the state-of-the-art methods, and can be further boosted when pre-trained on extra large-scale video-text data.
  arXiv  Detail & Related papers  (2022-07-18T14:18:13Z)
• PILED: An Identify-and-Localize Framework for Few-Shot Event Detection [79.66042333016478]
  In our study, we employ cloze prompts to elicit event-related knowledge from pretrained language models. We minimize the number of type-specific parameters, enabling our model to quickly adapt to event detection tasks for new types.
  arXiv  Detail & Related papers  (2022-02-15T18:01:39Z)
• Topic Detection and Tracking with Time-Aware Document Embeddings [23.348627263872842]
  We design a neural method that fuses temporal and textual information into a single representation of news documents for event detection. In the retrospective setting, we apply clustering algorithms to the time-aware embeddings and show substantial improvements over baselines on the News2013 data set. In the online streaming setting, we add our document encoder to an existing state-of-the-art TDT pipeline and demonstrate that it can benefit the overall performance.
  arXiv  Detail & Related papers  (2021-12-12T06:25:15Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Learning Constraints and Descriptive Segmentation for Subevent Detection [74.48201657623218]
  We propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction. We adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.
  arXiv  Detail & Related papers  (2021-09-13T20:50:37Z)
• Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction [17.548548562222766]
  We propose a reading comprehension framework for event extraction. By constructing proper query templates, our approach can effectively distill rich knowledge about tasks and label semantics. Our method achieves state-of-the-art performance on the ACE 2005 benchmark when trained with full supervision.
  arXiv  Detail & Related papers  (2020-10-21T21:48:39Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.