Abstract:
Causal relations are cause and effect connections where one occurrence a cause
influences the development of another event an effect. Causal relations extraction are a
method which extract unstructured causal texts into a meaningful cause-effect structured
categories. There is an alarming requirement to extract causal information from the
growing amount of data. Previously, many researchers have done causal relations
extraction for Amharic and English using machine learning and deep learning
approaches. Nevertheless, no study has been done to identify causal relations from Afan
Oromo text. This study aims to extract a causal relations from Afan Oromo text using a
deep learning approaches. We gathered Afan Oromo causal relations corpus from Afan
Oromo news sources, Oromia Communication Bureau, BBC News Afaan Oromoo, OBN
Afaan Oromoo, Ethiopian Press Agency /Bariisaa, Tajaajila Oduu Itoophiyaa, Kallacha
Oromiyaa, Waaltaa TV Afaan Oromoo and FBC Afaan Oromoo. The collected data were
annotated under four classes by an expert. Text preprocessing tasks applied on the
collected data. We have also used word2Vec and FastText word embedding to detect
word similarity for our dataset. We have applied the proposed three models, namely
LSTM, BiLSTM and CNN. The performance of causal relations extraction for Afan
Oromo text was assessed using evaluation metrics after selecting a hyper-parameter for
our model. The experimental results demonstrate that among the proposed models,
BiLSTM outperformed LSTM and CNN, scoring an accuracy of 93.37% compared to
LSTM 92.55% and CNN 84.57%, respectively.
