Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Most prior work on ML based lemmatization has focused on high resource languages, where data sets (word forms) are readily available. For languages which have no linguistic work available, especially on morphology or in languages where the computational realization of linguistic rules is complex and cumbersome, machine learning based lemmatizers are the way togo. In this paper, we devote our attention to lemmatisation for low resource, morphologically rich scheduled Indian languages using neural methods. Here, low resource means only a small number of word forms are available. We perform tests to analyse the variance in monolingual models’ performance on varying the corpus size and contextual morphological tag data for training. We show that monolingual approaches with data augmentation can give competitive accuracy even in the low resource setting, which augurs well for NLP in low resource setting.

Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. However, most of the prior work on this topic has focused on high resource languages. In this paper, we evaluate cross-lingual approaches for low resource languages, especially in the context of morphologically rich Indian languages. We test our model on six languages from two different families and develop linguistic insights into each model’s performance.

Dense word vectors or ‘word embeddings’ which encode semantic properties of words, have now become integral to NLP tasks like Machine Translation (MT), Question Answering (QA), Word Sense Disambiguation (WSD), and Information Retrieval (IR). In this paper, we use various existing approaches to create multiple word embeddings for 14 Indian languages. We place these embeddings for all these languages, viz., Assamese, Bengali, Gujarati, Hindi, Kannada, Konkani, Malayalam, Marathi, Nepali, Odiya, Punjabi, Sanskrit, Tamil, and Telugu in a single repository. Relatively newer approaches that emphasize catering to context (BERT, ELMo, etc.) have shown significant improvements, but require a large amount of resources to generate usable models. We release pre-trained embeddings generated using both contextual and non-contextual approaches. We also use MUSE and XLM to train cross-lingual embeddings for all pairs of the aforementioned languages. To show the efficacy of our embeddings, we evaluate our embedding models on XPOS, UPOS and NER tasks for all these languages. We release a total of 436 models using 8 different approaches. We hope they are useful for the resource-constrained Indian language NLP. The title of this paper refers to the famous novel “A Passage to India” by E.M. Forster, published initially in 1924.