Relation Extraction is an important subtask of Information Extraction that involves extracting significant facts from natural language text. Extracting structured information from the plaintext is the ultimate goal of IE systems. The Indian language content on the internet is increasing day to day. Extracting relevant information from this huge unstructured data is a challenging task especially when the business firms are interested in ascertaining public view on their products and processes. The primary objective of relation extraction systems is to find those entities which can be targeted through social networking and digital marketing. Cannibalisation of the product is nowadays done using these Social Networks. Different methods are proposed and experimented for Relation extraction problems. In this paper, we propose a Relation Extraction system using Convolutional Neural Networks. Deep learning based methods have produced state of the art results in many domains. Training and testing are conducted using the shared corpus provided by 'ARNEKT-IECSIL 2018' competition organisers. The evaluation results show that the proposed system could outperform most of the reported methods in the competition.
Anaphora resolution is one of the old problems in Natural Language Processing. It is the process of identifying the antecedent of an anaphoric expression in a natural language text. Most of the NLP applications such as text summarization, question answering, information extraction, machine translation etc. require the successful resolution of anaphors. In this paper, we propose a methodology for the resolution of pronominal anaphors present in Malayalam text document. The proposed methodology is a hybrid architecture employing machine learning and rule-based techniques. In our study, we have used a deep level tagger developed using a machine learning based algorithm. The deep level tagger provides detailed information regarding the number and gender of nouns in a text document. The morphological features of the language are effectively utilized for the computational analysis of Malayalam text. Despite using less amount of linguistic features, our system provides better results which can be utilized for higher level NLP tasks such as question answering, text summarization, machine translation, etc.
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