The automatic identification of planetary feature names in astronomy
publications presents numerous challenges. These features include craters,
defined as roughly circular depressions resulting from impact or volcanic
activity; dorsas, which are elongate raised structures or wrinkle ridges; and
lacus, small irregular patches of dark, smooth material on the Moon, referred
to as "lake" (Planetary Names Working Group, n.d.). Many feature names overlap
with places or people's names that they are named after, for example, Syria,
Tempe, Einstein, and Sagan, to name a few (U.S. Geological Survey, n.d.). Some
feature names have been used in many contexts, for instance, Apollo, which can
refer to mission, program, sample, astronaut, seismic, seismometers, core, era,
data, collection, instrument, and station, in addition to the crater on the
Moon. Some feature names can appear in the text as adjectives, like the lunar
craters Black, Green, and White. Some feature names in other contexts serve as
directions, like craters West and South on the Moon. Additionally, some
features share identical names across different celestial bodies, requiring
disambiguation, such as the Adams crater, which exists on both the Moon and
Mars. We present a multi-step pipeline combining rule-based filtering,
statistical relevance analysis, part-of-speech (POS) tagging, named entity
recognition (NER) model, hybrid keyword harvesting, knowledge graph (KG)
matching, and inference with a locally installed large language model (LLM) to
reliably identify planetary names despite these challenges. When evaluated on a
dataset of astronomy papers from the Astrophysics Data System (ADS), this
methodology achieves an F1-score over 0.97 in disambiguating planetary feature
names.

在天文学出版物中自动识别行星特征名称存在许多挑战，本研究采用多步骤流程结合规则过滤、统计相关性分析、命名实体识别模型、关键词采集、知识图谱匹配和大型语言模型进行推理，成功地在判定行星特征名称的歧义方面取得了 0.97 以上的 F1 分数。

天文论文中行星名称的识别：多步骤方法

Identifying Planetary Names in Astronomy Papers: A Multi-Step Approach

Craters are among the most studied geomorphic features in the Solar System
because they yield important information about the past and present geological
processes and provide information about the relative ages of observed geologic
formations. We present a method for automatic crater detection using advanced
machine learning to deal with the large amount of satellite imagery collected.
The challenge of automatically detecting craters comes from their is complex
surface because their shape erodes over time to blend into the surface.
Bandeira provided a seminal dataset that embodied this challenge that is still
an unsolved pattern recognition problem to this day. There has been work to
solve this challenge based on extracting shape and contrast features and then
applying classification models on those features. The limiting factor in this
existing work is the use of hand crafted filters on the image such as Gabor or
Sobel filters or Haar features. These hand crafted methods rely on domain
knowledge to construct. We would like to learn the optimal filters and features
based on training examples. In order to dynamically learn filters and features
we look to Convolutional Neural Networks (CNNs) which have shown their
dominance in computer vision. The power of CNNs is that they can learn image
filters which generate features for high accuracy classification.

本文利用卫星图像和机器学习技术，应用卷积神经网络对天体的撞击坑进行自动检测，并取得了良好效果。