B angla N et: Towards a W ord N et for B engali Language (2018)

Abstract: Despite being a popular language in the world, the Bengali language lacks in having a good wordnet. This restricts us to do NLP related research work in Bengali. Most of the today’s wordnets are developed by following expand approach. One of the key challenges of this approach is the cross-lingual word-sense disambiguation. In our research work, we make semantic relation between Bengali wordnet and Princeton WordNet based on well-established research work in other languages. The algorithm will derive relations between concepts as well. One of our key objectives is to provide a panel for lexicographers so that they can validate and contribute to the wordnet.

## introduction the princeton wordnet (pwn) @xcite is one of the most semantically rich english lexical database which is widely used as a resource in many research and development. it is not only an important resource for nlp applications in each language, but also for inter-linking wordnets of different languages to develop multilingual applications to overcome the language barrier. in the present, there are roughly 6,500 languages 1 . among those, bengali is the 7th most popular language 2 in the world. yet, there is a lack of work for bengali wordnet. global word-net association (gwa) has enlisted almost all wordnets in several levels depending on availability and how rich it is. at first level, there are 34 open multi-lingual wordnet 3 that are merged into global wordnet grid. but in spite of being a popular language, bengali is not one of them. gwa also enlist other available wordnets. among those 80 wordnets, there are two bengali wordnets which are developed in india. in this research work, a baseline for banglanet has been developed which is a wordnet for the bengali language. to link the wordnet with princeton wordnet, semi-automatic crosslingual sense mapping approach is used. we align the princeton wordnet synset into a bilingual dictionary through the english equivalent and its part-of-speech (pos). manual translation and link-up can also be employed after the alignment. this paper covers previous works for other wordnets including previous attempts of developing bengali wordnet, describe initiative taken for banglanet and our design and execution process in depth. lastly, analysis of resultant lexical database has been presented. we aim to include banglanet into globalwordnet in future. intending to doing so, relation with princeton wordnet is maintained as much as possible as per the convention. additionally, a web-based collaborative tool, called oikotan which is banglanet lexicography development panel (ldp) has been developed for revising the result of synset assignment and provide a framework to create banglanet via the linkage with synsets. ## architecture it has been discussed above that expand approach is followed to construct the banglanet by translating the synsets in the princeton wordnet to the bengali language. both automatic and manual methods are applied in the process. ambiguity is one of the concerns for automatic concept mapping. this crosslingual ambiguity can come in different form. for instance -one-to-one, one-to-many, manyto-one, many-to-many. in this research work, uni-directional ambiguity in one-to-one and one-to-many has been addressed. based on our research on other languages' wordnet and past works in bengali wordnet, this paper proposes to follow methodology described in fig 1 for banglanet development. ## results and analysis in the initial dataset, there were 27239 unique concepts. these concepts are represented using 40158 unique words tagged with different parts of speech. to link this 4665 concepts with princeton wordnet, 3729 princeton wordnet concepts are used. that means, there are cross multiple concepts within two wordnet. cross-lingual word-sense disambiguation can be shown using another example. for the word " খেলা ভুক্তক াগী ফুলেমি " there are two concept available in bengali. in english it has two concepts too. cauliflower.n.01 a plant having a large edible head of crowded white flower buds cauliflower.n.02 compact head of undevel-oped white flowers the algorithm predicted both english concepts for the two concepts available. for খেলা ভুক্তক াগী ফুলেমি .n.01 probability score for english concepts are 4.4419589754 and 4.4419589754 respectively. on the other hand, খেলা ভুক্তক াগী ফুলেমি .n.02 score is 6.84959684439 and 6.20774295822. it is observed that for both cases these scores are too close to prioritize probability. although the algorithm used in banglanet is directed from bengali to english synset matching, this development can also be implied from another way around. in that case, bengali word which represents a particular concept in princeton wordnet can be used to verify and add more confidence to concept linking. as a result, more link up can be achieved. our initial synset contains gloss. but our approach does not take gloss into consideration. as a consequence, banglanet can be expanded using the same approach in future even if gloss for a synset is not available. ## conclusion developing wordnet is an immense task. it is our distinct pleasure that in this research work, a basic layer of the system has been laid down for bengali wordnet from where further development can be made. suggestion generation task for validation can be achievable through the result of this research work. our result analysis shows that around 5000 words from initially collected data are automatically linked up with princeton wordnet. although there is a long way to go in the development of bengali wordnet, this research work is starting stage for further development.