CIC-IPN@INLI2018: Indian Native Language Identification

The assumption is that, bidirectional LSTM can capture the entire document features regarding language model into x word dt and n-gram features for convolu- tional neural network into

In this paper, we propose to use Hyperdimensional Computing (HDC) as supervised learning for identifying Indian Native Languages from the user’s social media comments written

We thus evaluated and extended the stacked sentence- document architecture proposed by (Cimino and Dell’Orletta, 2017). In addition to the linguistic features, they proposed a

We chose these resources since they are available both for Italian and French (and several other languages). For the alignement of the semantic spaces of the two lan- guages we

Given a corpus with comments in English from various Facebook newspapers pages, the objective of the task is to identify the na- tive language among the following six Indian

We have ex- tracted TF-IDF feature vectors from the given document and used SVM classifier to identify the native language of the document given by shared task on Indian Native

The set of BOW features along with the class labels namely Tamil, Hindi, Kannada, Malayalam, Bengali and Telugu from training data are used to build a model using a simple

In this paper we propose a deep neural network for this task using hierarchical paragraph encoder with attention mechanism to identify relevant features over tendencies and errors