Identification of Idiomatic Expressions in K onkani Language Using Neural Networks (2024)

Abstract: The task of multi-word expressions identification and processing has posed a remarkable challenge to the natural language processing applications. One related subtask in this arena is correct labelling of the sentences with the presence of idiomatic expressions as either literal or idiomatic sense. The regional Indian language Konkani spoken in the states located in the west coast of India lacks in the research in idiom processing tasks. We aim at bridging this gap through a contribution to idiom identification method in Konkani language. This paper classifies the idiomatic expression usage in Konkani language as idiomatic or literal usage using a neural network-based setup. The developed system was able to successfully perform the identification task with an accuracy of 79.5 % and F1-score of 0.77.

## introduction india is a vast country with huge number of different languages spoken from one corner of the country to another. as compared to the deep learning research available in english language with respect to various arenas like questionanswering, parts-of-speech tagging, idioms and metaphor detection and paraphrasing, etc., the indian languages have not been studied in terms of natural language processing in that depth. this might be due to lack of digital resources in the past. but that gap is being filled now with large number of datasets and resources in indian languages surfacing in recent years through various projects, making the deep learning research feasible in many of the indian languages. research on classification of language constructs like idioms, similes, metaphors, etc. is still in its base form with current research in this context still referring to use of rulebased methods. this motivated us to test the application task of identification of idiomatic sense in the statements with the presence of potentially idiomatic expressions in konkani language using the neural network. konkani is a language spoken by over 2.5m speakers on the west coastal states of india @xcite . the potentially idiomatic expressions refer to class of idiomatic expressions that can be used in a sentence in both literal way as well as the idiomatically sensed way. this makes it difficult to get the clear semantic understanding of the statement. the translation of such sentences becomes a challenging machine translation task. consider for example the idiom: खोबरें करप kʰɔbrɛ ̃ kərəp this idiom literally means -"to make a product out of crushed dry coconut". this multi-word expression is used in the idiomatic way to indicate "ruining or messing up of something". when used in this sense, this idiom has no semantic relation to the constituent words that refer to the product made out of dry coconut. the example sentence using this expression in the idiomatic sense is shown in sentence: ताणें सगल्या कामाचें खोबरे करून दवरलाां , आताां आमकाां काम जाग्यार उडोवपाक खू ब त्रास जाता. with this system that can identify the sense in which these idiomatic expressions have been used can be very useful in such a scenario. the paper is organized into 6 sections beginning with the abstract. this is followed by introduction of the basic idea of this paper. further, the related ## related work the methods for detection of idiomatic senses in english language sentences range from the use of rule-based techniques to the use of deep neural networks methods. the rule-based techniques focused on the type and token detection depending on the lexical properties of the idioms like lexical fixedness @xcite . these rule-based techniques for idiom identification were also tested for the indian language hindi by @xcite . further methods used the statistical techniques of distributional semantics of a given sentence for idiom identification @xcite . the deep learning techniques used for idiom identification in english language include use of a neural networks with added input features like parts of speech tagging and the ensemble method that incorporates multiple modules of networks for the classification task. the most recent shared task on the idiom identification in english and portuguese language was conducted by @xcite to determine idioms in few-shot, zero-shot, one-shot and fine-tuning settings over the given datasets. the task was to use various methodologies for identification over the provided datasets. the dataset consisted of statements with potential idiomatic usages along with the corresponding previous and the next sentences for the given statement. each statement was given the classification as idiomatic or literal. various methods used for this task include use of mbert along with lstm and textcnn @xcite , bert finetuning over feature-based sentence transformer @xcite , span-wise identification over bert model @xcite , large language models @xcite for classification of idiomatic sense in a sentence. another mbert and bilstm combination network @xcite was used to identify idioms in 10 different languages. the task of identification as well as localization of potentially idiomatic expressions in a sentence by @xcite used multi-level neural model along with attention mechanism combined with the contextual embedding references. other methods for improved idiom identification in english include the ensemble method to incorporate common knowledge into the bert structure @xcite . although wide range of efforts have been made in this area, they are all mainly limited to english and few other languages like portuguese. corresponding research in low-resourced indian languages is lacking in this arena. the computational research currently done on idiomatic constructions in hindi includes the use of a rule-base constructed manually based on the idiom properties in hindi that is used to determine possibility of idiomaticity. @xcite . ## proposed methodology this paper builds a sequential model through a bidirectional long short term memory neural network for the purpose of idiomatic statement identification in konkani language. the basic architectural dimensions of the system are depicted in figure 1 . the method depends on the feature extraction of the sentences in the training data through static embeddings using the sequence information in both directions. the static embeddings generated from the data using one-hot representation as the base embedding were further passed on to a bilstm network for extraction of the features as per the binary classification associated with the labels 'idiom' and 'literal' provided in the training dataset. this model was trained using the gradient approach with adam optimizer based on the logarithmic loss function. the output from this layer was connected to the dense layer with the sigmoid activation to derive the final output classification in form of binary labels 'idiom' or 'literal'. ## results and analysis the result of this experiment proved the efficiency of using simple static embeddings over the bilstm layer for the purpose of classification and usage label identification of the statement containing the idiomatic expression. the system created was able to classify any given sentence as idiom or literal with an accuracy of 79.5%. the dataset being 2% more skewed and imbalanced towards idiom label classification, the f1-score was calculated to be 0.77. ## conclusion this paper uses a bilstm network over the static embeddings to classify the sense of any idiomatic expression in a konkani sentence. the created system was able to successfully classify the usage of the idiomatic expressions in the konkani sentence into the binary labels -'idiom' and 'literal' with reasonable accuracy. this model serves as the first step using the neural networks towards the research of multi-word expressions in konkani language. the future work can include the inculcation of contextual embeddings and attention mechanisms for the classification and processing of idiomatic statements.