Unsupervised Location Mapping for Narrative Corpora

• URL: http://arxiv.org/abs/2504.05954v1
• Date: Tue, 08 Apr 2025 12:06:47 GMT
• Title: Unsupervised Location Mapping for Narrative Corpora
• Authors: Eitan Wagner, Renana Keydar, Omri Abend,
• Abstract summary: Task seeks to map the trajectory of an individual narrative on a spatial map of locations in which a large set of narratives take place.<n>We propose a pipeline for this task in a completely unsupervised manner without predefining the set of labels.<n>We test our method on two different domains: (1) Holocaust testimonies and (2) Lake District writing, namely multi-century literature on travels in the English Lake District.
• Score: 14.90042250991032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents the task of unsupervised location mapping, which seeks to map the trajectory of an individual narrative on a spatial map of locations in which a large set of narratives take place. Despite the fundamentality and generality of the task, very little work addressed the spatial mapping of narrative texts. The task consists of two parts: (1) inducing a ``map'' with the locations mentioned in a set of texts, and (2) extracting a trajectory from a single narrative and positioning it on the map. Following recent advances in increasing the context length of large language models, we propose a pipeline for this task in a completely unsupervised manner without predefining the set of labels. We test our method on two different domains: (1) Holocaust testimonies and (2) Lake District writing, namely multi-century literature on travels in the English Lake District. We perform both intrinsic and extrinsic evaluations for the task, with encouraging results, thereby setting a benchmark and evaluation practices for the task, as well as highlighting challenges.

Related papers

• Map2Text: New Content Generation from Low-Dimensional Visualizations [60.02149343347818]
  We introduce Map2Text, a novel task that translates spatial coordinates within low-dimensional visualizations into new, coherent, and accurately aligned textual content.<n>This allows users to explore and navigate undiscovered information embedded in these spatial layouts interactively and intuitively.
  arXiv  Detail & Related papers  (2024-12-24T20:16:13Z)
• Tag Map: A Text-Based Map for Spatial Reasoning and Navigation with Large Language Models [15.454856838083511]
  Large Language Models (LLM) have emerged as a tool for robots to generate task plans using common sense reasoning. Recent works have shifted from explicit maps with fixed semantic classes to implicit open vocabulary maps. We propose an explicit text-based map that can represent thousands of semantic classes while easily integrating with LLMs.
  arXiv  Detail & Related papers  (2024-09-23T18:26:19Z)
• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• PlotMap: Automated Layout Design for Building Game Worlds [4.74497343690049]
  We introduce an extra layer of plot facility layout design that is independent of the underlying map generation method in a world-building pipeline. We present two methods for solving these tasks automatically: an evolutionary computation based approach throughCMA-ES, and a Reinforcement Learning (RL) based approach. We develop a method of generating datasets of facility layout tasks, create a gym-like environment for experimenting with and evaluating different methods, and further analyze the two methods with comprehensive experiments.
  arXiv  Detail & Related papers  (2023-09-26T20:13:10Z)
• Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking [61.60169764507917]
  Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. We propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines.
  arXiv  Detail & Related papers  (2023-09-04T13:44:50Z)
• MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture [16.817025300716796]
  We propose a semantic segmentation method, MTLSegFormer, which combines multi-task learning and attention mechanisms. We tested the performance in two challenging problems with correlated tasks and observed a significant improvement in accuracy.
  arXiv  Detail & Related papers  (2023-05-04T13:19:43Z)
• SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks [88.4408774253634]
  Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community. There are not nearly as many SLU task benchmarks, and many of the existing ones use data that is not freely available to all researchers. Recent work has begun to introduce such benchmark for several tasks.
  arXiv  Detail & Related papers  (2022-12-20T18:39:59Z)
• Connecting What to Say With Where to Look by Modeling Human Attention Traces [30.8226861256742]
  We introduce a unified framework to jointly model images, text, and human attention traces. We propose two novel tasks: (1) predict a trace given an image and caption (i.e., visual grounding), and (2) predict a caption and a trace given only an image.
  arXiv  Detail & Related papers  (2021-05-12T20:53:30Z)
• Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting [168.91748514706995]
  We propose two novel cross-modal translation pre-text tasks for self-supervised feature learning: Vectorization and Rasterization. Our learned encoder modules benefit both-based and vector-based downstream approaches to analysing hand-drawn data.
  arXiv  Detail & Related papers  (2021-03-25T09:47:18Z)
• ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection [147.10751375922035]
  We propose the ContourNet, which effectively handles false positives and large scale variance of scene texts. Our method effectively suppresses these false positives by only outputting predictions with high response value in both directions.
  arXiv  Detail & Related papers  (2020-04-10T08:15:23Z)

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.