This paper presents an exploration of Long Short-Term Memory (LSTM) networks
in the realm of text generation, focusing on the utilization of historical
datasets for Shakespeare and Nietzsche. LSTMs, known for their effectiveness in
handling sequential data, are applied here to model complex language patterns
and structures inherent in historical texts. The study demonstrates that
LSTM-based models, when trained on historical datasets, can not only generate
text that is linguistically rich and contextually relevant but also provide
insights into the evolution of language patterns over time. The finding
presents models that are highly accurate and efficient in predicting text from
works of Nietzsche, with low loss values and a training time of 100 iterations.
The accuracy of the model is 0.9521, indicating high accuracy. The loss of the
model is 0.2518, indicating its effectiveness. The accuracy of the model in
predicting text from the work of Shakespeare is 0.9125, indicating a low error
rate. The training time of the model is 100, mirroring the efficiency of the
Nietzsche dataset. This efficiency demonstrates the effectiveness of the model
design and training methodology, especially when handling complex literary
texts. This research contributes to the field of natural language processing by
showcasing the versatility of LSTM networks in text generation and offering a
pathway for future explorations in historical linguistics and beyond.

该论文探讨了在文本生成中应用长短期记忆（LSTM）网络，重点关注历史数据集在莎士比亚和尼采作品中的应用。研究表明，在历史数据集上训练的基于 LSTM 的模型不仅可以生成语言丰富且相关的文本，还能提供语言模式随时间演化的见解。该研究为自然语言处理领域做出了贡献，展示了 LSTM 网络在文本生成中的多样性，并为历史语言学及其他领域的未来探索提供了途径。

基于 LSTM 的文本生成：关于历史数据的研究

LSTM-Based Text Generation: A Study on Historical Datasets

NLP methods can aid historians in analyzing textual materials in greater
volumes than manually feasible. Developing such methods poses substantial
challenges though. First, acquiring large, annotated historical datasets is
difficult, as only domain experts can reliably label them. Second, most
available off-the-shelf NLP models are trained on modern language texts,
rendering them significantly less effective when applied to historical corpora.
This is particularly problematic for less well studied tasks, and for languages
other than English. This paper addresses these challenges while focusing on the
under-explored task of event extraction from a novel domain of historical
texts. We introduce a new multilingual dataset in English, French, and Dutch
composed of newspaper ads from the early modern colonial period reporting on
enslaved people who liberated themselves from enslavement. We find that: 1)
even with scarce annotated data, it is possible to achieve surprisingly good
results by formulating the problem as an extractive QA task and leveraging
existing datasets and models for modern languages; and 2) cross-lingual
low-resource learning for historical languages is highly challenging, and
machine translation of the historical datasets to the considered target
languages is, in practice, often the best-performing solution.

这篇研究探讨如何从历史文本中提取事件，并介绍了一个在英语、法语和荷兰语中报导解放奴隶的早期殖民时期报纸广告所组成的新的多语言数据集，作者发现通过将问题描述为提取式 QA 任务，并利用现有的现代语言数据集和模型，即使数据有限，也能实现令人惊讶的好结果；对于历史语言的跨语言低资源学习也具有高度的挑战性，实际上，历史数据集的机器翻译到目标语言经常是最好的解决方案。

历史报纸广告的多语言事件抽取

Multilingual Event Extraction from Historical Newspaper Adverts

This paper presents an unsupervised approach for writer retrieval based on
clustering SIFT descriptors detected at keypoint locations resulting in
pseudo-cluster labels. With those cluster labels, a residual network followed
by our proposed NetRVLAD, an encoding layer with reduced complexity compared to
NetVLAD, is trained on 32x32 patches at keypoint locations. Additionally, we
suggest a graph-based reranking algorithm called SGR to exploit similarities of
the page embeddings to boost the retrieval performance. Our approach is
evaluated on two historical datasets (Historical-WI and HisIR19). We include an
evaluation of different backbones and NetRVLAD. It competes with related work
on historical datasets without using explicit encodings. We set a new
State-of-the-art on both datasets by applying our reranking scheme and show
that our approach achieves comparable performance on a modern dataset as well.

本文提出了一种基于无监督方法的作者检索方法，其采用 SIFT 描述符进行聚类，并通过 NetRVLAD 进行编码，同时提出了一种基于图的重新排序算法（SGR）以提高检索性能，该方法在两个历史数据集上表现出色。