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Corpus and Evaluation of Handwriting Recognition of
Historical Genealogical Records
Patrick Shone, Heath Nielson, Mark WardFamilySearch and LDS Publishing Services Department
50 E North Temple, Salt
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How to fill out corpus and evaluation of
How to fill out corpus and evaluation of:
01
Identify the purpose: Before starting, determine the purpose of the corpus and evaluation. Are you creating a corpus for linguistic research, machine learning training data, or language model building? Understanding the purpose will help guide the process.
02
Gather relevant data: Collect a diverse range of texts or spoken recordings that are representative of the language or subject matter that the corpus aims to cover. This could involve gathering documents, transcribing audio, or scraping data from online sources.
03
Clean and preprocess the data: Since the gathered data may contain noise, errors, or irrelevant information, it is crucial to clean and preprocess it. This involves removing duplicate entries, correcting errors, standardizing formatting, and applying linguistic processing (such as tokenization or lemmatization).
04
Organize the corpus: Decide on a suitable structure for the corpus. You may categorize the data into different topics or themes, assign metadata to each entry, or organize it based on linguistic features or genres.
05
Annotate the corpus: Depending on the purpose of the corpus, you may need to annotate it with additional information such as part-of-speech tags, named entities, syntactic parses, or sentiment labels. Annotation can be done manually or through automated tools, but it is essential for enhancing the quality and usefulness of the corpus.
Evaluation of the corpus:
01
Define evaluation criteria: Determine the metrics or criteria that you will use to evaluate the quality and effectiveness of the created corpus. This could include factors like coverage, representativeness, diversity, correctness, completeness, or compatibility with specific applications.
02
Select evaluation methods: Choose appropriate methods for evaluating the corpus based on the defined criteria. This may involve conducting human evaluations, comparing the corpus against existing benchmark datasets, or using computational techniques to analyze linguistic properties.
03
Establish an evaluation process: Define a clear and replicable process for evaluating the corpus. This could include guidelines for annotators, specific tasks for evaluators, or steps for analyzing the results.
04
Gather feedback: Seek feedback from experts or users in the domain for which the corpus was created. Their insights can help identify areas for improvement and validate the usefulness of the corpus.
Who needs corpus and evaluation of:
01
Linguists and language researchers: Corpus data and evaluations are valuable resources for linguists studying language patterns, grammar, sociolinguistics, or dialects. It helps them analyze and draw conclusions about language usage.
02
Machine learning practitioners: Corpus data is crucial for training and testing machine learning models, such as natural language processing algorithms, sentiment analysis systems, or chatbots. The evaluation provides a benchmark to assess the performance of these models.
03
Language model developers: Corpus data plays a vital role in training language models like neural networks, transformers, or statistical models. Evaluations help developers fine-tune and optimize these models for different tasks and languages.
In conclusion, properly filling out a corpus and evaluating its quality is essential for various individuals and industries, including linguists, machine learning practitioners, and language model developers. By following the steps outlined above, you can create a useful and effective corpus while being able to assess its strengths and weaknesses through evaluation.
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