印地语词干提取NLP项目：词-词干标注数据集及提取方法问询

Hey there! Let's tackle your two questions about Hindi vocabulary-stem mapping and labeled datasets for your NLP project:

Here are practical, hands-on ways to build or obtain this mapping:

• Reverse-engineer with existing Hindi stemmers: Tools like nltk.stem.hindi.HindiStemmer in NLTK are a great starting point. First, gather a large corpus of Hindi words—you can pull from Hindi Wikipedia dumps, open-source Hindi news corpora, or public Hindi text collections. Run the stemmer over this corpus to generate word-stem pairs. Just remember to manually spot-check a portion of results, since automatic stemmers might mess up irregularly inflected words.
• Extract from Hindi morphological dictionaries: Resources like Hindi WordNet include root word information for most entries. You can parse the structure of such dictionaries to pull out direct word-stem mappings. Each synset in Hindi WordNet links to its root, which gives you a reliable base of labeled pairs.
• Manual annotation + semi-supervised expansion: If you can't find enough ready-made data, start by manually labeling a set of high-frequency words (focus on terms relevant to your project first). Then use this small labeled dataset to train a lightweight model, which you can then use to auto-label more unannotated words. Iterate this process, correcting errors each time, to grow your dataset gradually.

For your deep learning project, here are actionable sources and strategies:

• Build from public corpora + morphological analyzers: Combine open Hindi text sources (like Project Gutenberg's Hindi texts, or government-published Hindi documents) with dedicated Hindi morphological analyzers (such as those developed by India's Center for Language Technology Research or IIIT Hyderabad). These analyzers can generate stem annotations automatically. While there might be some noise, you can filter out low-confidence pairs and manually validate key entries to get a usable training dataset.
• Check academic research repositories: Many NLP conferences (ACL, EMNLP, etc.) and Indian research institutions (like IIT Bombay, IIIT Hyderabad) release open datasets for Hindi morphological tasks. Look through the project pages of recent Hindi NLP papers or the labs' open-source sections—you'll often find pre-labeled stem datasets shared for research purposes.
• Crowdsource custom annotations: If you have a small budget, use crowdsourcing platforms that target Hindi-speaking users to create a domain-specific labeled dataset. This is especially useful if your project focuses on niche terminology (like medical or technical Hindi), as you can tailor the annotation task to your exact needs and get high-quality, relevant data.
• Leverage multilingual transfer: Since Hindi shares many roots with Sanskrit, you can adapt stem datasets from Sanskrit (which has more extensive morphological resources) to Hindi. You'll need some basic linguistic knowledge to adjust for sound changes and modern word variations, but this can be a viable workaround if pure Hindi data is scarce.

内容的提问来源于stack exchange，提问作者vkSinha