Disambiguating Numeral Sequences to Decipher Ancient Accounting Corpora

• URL: http://arxiv.org/abs/2502.00090v1
• Date: Fri, 31 Jan 2025 18:10:31 GMT
• Title: Disambiguating Numeral Sequences to Decipher Ancient Accounting Corpora
• Authors: Logan Born, M. Willis Monroe, Kathryn Kelley, Anoop Sarkar,
• Abstract summary: We study the ancient and partially-deciphered proto-Elamite (PE) script. Written numerals can have up to four distinct readings depending on the system that is used to read them. We consider the task of disambiguating between these readings in order to determine the values of the numeric quantities recorded in this corpus.
• Score: 7.530971114462749
• License:
• Abstract: A numeration system encodes abstract numeric quantities as concrete strings of written characters. The numeration systems used by modern scripts tend to be precise and unambiguous, but this was not so for the ancient and partially-deciphered proto-Elamite (PE) script, where written numerals can have up to four distinct readings depending on the system that is used to read them. We consider the task of disambiguating between these readings in order to determine the values of the numeric quantities recorded in this corpus. We algorithmically extract a list of possible readings for each PE numeral notation, and contribute two disambiguation techniques based on structural properties of the original documents and classifiers learned with the bootstrapping algorithm. We also contribute a test set for evaluating disambiguation techniques, as well as a novel approach to cautious rule selection for bootstrapped classifiers. Our analysis confirms existing intuitions about this script and reveals previously-unknown correlations between tablet content and numeral magnitude. This work is crucial to understanding and deciphering PE, as the corpus is heavily accounting-focused and contains many more numeric tokens than tokens of text.

Related papers

• Batching BPE Tokenization Merges [55.2480439325792]
  BatchBPE is an open-source pure Python implementation of the Byte Pair algorithm. It is used to train a high quality tokenizer on a basic laptop.
  arXiv  Detail & Related papers  (2024-08-05T09:37:21Z)
• Out of Length Text Recognition with Sub-String Matching [54.63761108308825]
  In this paper, we term this task Out of Length (OOL) text recognition. We propose a novel method called OOL Text Recognition with sub-String Matching (SMTR) SMTR comprises two cross-attention-based modules: one encodes a sub-string containing multiple characters into next and previous queries, and the other employs the queries to attend to the image features.
  arXiv  Detail & Related papers  (2024-07-17T05:02:17Z)
• A cost minimization approach to fix the vocabulary size in a tokenizer for an End-to-End ASR system [10.70500939394669]
  tokenization algorithms like Byte Pair Piece (BPE) and WordPiece are popular in identifying the tokens that are used in the overall training process of the speech recognition system. We show through experiments on LibriSpeech 100 hour set that the performance of an end-to-end ASR system improves when the number of tokens are chosen carefully.
  arXiv  Detail & Related papers  (2024-04-29T12:16:21Z)
• Attributable and Scalable Opinion Summarization [79.87892048285819]
  We generate abstractive summaries by decoding frequent encodings, and extractive summaries by selecting the sentences assigned to the same frequent encodings. Our method is attributable, because the model identifies sentences used to generate the summary as part of the summarization process. It scales easily to many hundreds of input reviews, because aggregation is performed in the latent space rather than over long sequences of tokens.
  arXiv  Detail & Related papers  (2023-05-19T11:30:37Z)
• Lexical Complexity Prediction: An Overview [13.224233182417636]
  The occurrence of unknown words in texts significantly hinders reading comprehension. computational modelling has been applied to identify complex words in texts and substitute them for simpler alternatives. We present an overview of computational approaches to lexical complexity prediction focusing on the work carried out on English data.
  arXiv  Detail & Related papers  (2023-03-08T19:35:08Z)
• Siamese based Neural Network for Offline Writer Identification on word level data [7.747239584541488]
  We propose a novel scheme to identify the author of a document based on the input word image. Our method is text independent and does not impose any constraint on the size of the input image under examination.
  arXiv  Detail & Related papers  (2022-11-17T10:01:46Z)
• Text Summarization with Oracle Expectation [88.39032981994535]
  Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document. Most summarization datasets do not come with gold labels indicating whether document sentences are summary-worthy. We propose a simple yet effective labeling algorithm that creates soft, expectation-based sentence labels.
  arXiv  Detail & Related papers  (2022-09-26T14:10:08Z)
• LeQua@CLEF2022: Learning to Quantify [76.22817970624875]
  LeQua 2022 is a new lab for the evaluation of methods for learning to quantify'' in textual datasets. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting.
  arXiv  Detail & Related papers  (2021-11-22T14:54:20Z)
• Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution [74.27826764855911]
  We employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. Our experiments, carried out on three different datasets, using two different machine learning methods, show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors.
  arXiv  Detail & Related papers  (2021-10-27T06:25:31Z)
• An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings [9.950131528559211]
  An end-to-end solution for handwritten numeral string recognition is proposed. The main contribution is to avoid string-based methods for preprocessing and segmentation. A robust experimental protocol based on several numeral string datasets has shown that the proposed method is a feasible end-to-end solution.
  arXiv  Detail & Related papers  (2020-03-28T16:51:00Z)
• CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data [13.224233182417636]
  This paper presents the first English dataset for continuous lexical complexity prediction. We use a 5-point Likert scale scheme to annotate complex words in texts from three sources/domains: the Bible, Europarl, and biomedical texts.
  arXiv  Detail & Related papers  (2020-03-16T03:54:22Z)

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.