Technical Domain Identification using word2vec and B i LSTM (2020)

Abstract: Coarse-grained and Fine-grained classification tasks are mostly based on sentiment or basic emotion analysis. Now, switching from emotion and sentiment analysis to another domain, in this paper, we are going to work on technical domain identification. The task is to identify the technical domain of a given English text. In the case of Coarse-grained domain classification, such a piece of text provides information about specific Coarse-grained technical domains like Computer Science, Physics, Math, etc, and in Fine-grained domain classification, Fine-grained subdomains for Computer science domain, it can be like Artificial Intelligence, Algorithm, Computer Architecture, Computer Networks, Database Management system, etc. To do the task, Word2Vec skip-gram model is used for word embedding, later, applied the Bidirectional Long Short Term memory (BiLSTM) model to classify Coarse-grained domains and Fine-grained sub-domains. To evaluate the performance of the approached model accuracy, precision, recall, and F1-score have been applied.

## methodology in this section, dataset, data preprocessing, word embedding and the structure of the bilstm with word2vec model will be discussed. approached architechture of bilstm with word2vec is shown in figure 2.1 dataset here, technical domain identification dataset foot_1 in its coarse-grained domain classification -english (subtask-1a) form and fine-grained domain classification -computer science (subtask-2a) form has been used. ## result and conclusion as, it was a prediction task on test dataset and presently, we don't have labeled test dataset, dev dataset is used here to evaluate the model performance. from the after applying preprocessing on the texts of subtask 2a dataset, we get maximum sentence length is 266 but other texts are not that long except one. here, we trained word2vec model only with the given training dataset which is very small dataset to perform good embedding. word embedding training gives us 10,722 unique words in subtask 1a and 7,397 in subtask 2a. quite obvious, dealing with fine grained dataset compare to coarse grained dataset is somehow challenging as vocabulary size is very small. word embedding seems very important here, in most of the cases pre-trained word embedding such as google news dataset foot_2 (about 100 billion words) is used, which contains 300-dimensional vectors for 3 million words and phrases. the archive is available here: googlenews-vectors-negative300. bin.gz. but in our case, some words like "streptococcus", "poly-merization"," hessian", "kyoto" are missing from the large google news dataset. those words are very important to