All the unconstrained submission will used for the academic discussion during the session. Team will be doing best in all the language pairs using only our data (constrained) will be the winner. Accordingly they have to mention those resources explicitly in their task-report.
Unconstrained: Means the participant team can use any external resource (available POS tagger, NER, Parser, and any additional data) to train their system. More details about the tagset could be found in our RANLP paper.Įach team may submit up to 4 runs, one constrained (*2 for fine-grained and coarse-grained) and one unconstrained (*2 for fine-grained and coarse-grained).Ĭonstrained: Means the participant team is only allowed to use our corpus for the training. Fine-grained tagset and their mapping to coarse-grained tagset is mentioned in the Table 1. There will be two tracks: fine grained a coarse-grained tagset ( Google universal tagset). The contest task is to predict POS tags at word level, whereas language tags (en, hi/bn/te, univ, ne, undef) at word level will be given. WhatsApp messages are relatively much smaller than Facebook and Twitter messahes, therefore more challenging. Possibly this is the first time NLP related issue on WhatsApp messages is being discussed. We are releasing code-mixed WhatsApp data for 3 language pairs: English-Hindi, English-Bengali, and English-Telugu. Shortlisted candidates will present their techniques and results in a special session at ICON 2016. Efficiency will be measured in terms of Precision, Recall, and F-measure.
The datasets may be provided with some additional information like the languages of each word. English-Hindi, English-Bengali, and English-Telugu language mixing will be explored.
Participants will be provided training, development and test data to report the efficiency of their POS tagging system. This year we will continue the last year’s POS tagging shared-task on three widely spoken Indian languages (Hindi, Bengali, and Telugu), mixed with English.Įxample 1: ICON 2016 Varanasi me hold hoga! Great chance to see the pracheen nagari! Part-of-speech (POS) tagging is an essential prerequisite for any kind of NLP applications. Hence, Indians are multi-lingual by adaptation and necessity, and frequently change and mix languages in social media contexts, which poses additional difficulties for automatic Indian social media text processing. Language diversity and dialect changes instigate frequent code-mixing in India. India is home to several hundred languages. While it is clear that English still is the principal language for social media communications, there is a growing need to develop technologies for other languages, including Indian languages. Instead, they use phonetic typing/ roman script/ transliteration and frequently insert English words or phrases through code-mixing and anglicisms (see the following example ), and often mix multiple languages to express their thoughts. Non-English speakers, especially Indians, do not always use Unicode to write something in social media in ILs. The evolution of social media texts such as blogs, micro-blogs (e.g., Twitter), WhatsApp, and chats (e.g., Facebook messages) has created many new opportunities for information access and language technology, but also many new challenges, making it one of the prime present-day research areas.
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