Abstract:
The interaction between human to computer is a daily occurrences. Due to the rapid change of
technologies, for example, HCI (Human computer Interaction) and NLP (Natural language
processing) streamline the easiness and effectiveness of computer systems that can better
communicate and assist human beings. For instance, Part of speech tagging is one attempt in the
effort of understanding human language to identify the role of a word in a given context.
There are a significant amount of work have been done to address part of speech disambiguation
problems towards sematic languages. For example, Part of speech tagging for Tigrigna has been
done using hybrid approach; HMM tagger combined with the rule based tagger. Unlike the
aforementioned approaches, a neural network approach is an effective method to achieve a better
performance towards part of speech tagger for Tigrigna. Therefore, the main objective of the
study is developing an effective part of speech tagger scheme for Tigrigna words to attain a
better performance.
To achieve the previously specified objective, a neural network based part of speech tagging for Tigrigna
scheme is proposed. The tagger is used Back propagation learning algorithm in order to train the
neural network.
In order to evaluate the performance of the proposed scheme, an extensive experiment is
conducted using a corpus comprised of the training set and the prepared testing set. The
performance of the Tigrigna MLP tagger in the 75% of the dataset and 25% of the dataset is
90.726% and 85.693% using 10-fold cross validation technique respectively while the overall
performance of the MLP tagger on the whole datasets which can be used K fold technique is
93.849% which takes a total time about 2 hour to train.
The experiment results demonstrate that MLP tagger achieve an efficient performance using
prepared testing set. However, this result would improve better if there is more dataset for
training and testing.
