Moving Towards Open Set Incremental Learning: Readily Discovering New Authors

The classification of textual data often yields important information. Most classifiers work in a closed world setting where the classifier is trained on a known corpus, and then it is tested on unseen examples that belong to one of the classes seen during training. Despite the usefulness of this design, often there is a need to classify unseen examples that do not belong to any of the classes on which the classifier was trained. This paper describes the open set scenario where unseen examples from previously unseen classes are handled while testing. This further examines a process of enhanced open set classification with a deep neural network that discovers new classes by clustering the examples identified as belonging to unknown classes, followed by a process of retraining the classifier with newly recognized classes. Through this process the model moves to an incremental learning model where it continuously finds and learns from novel classes of data that have been identified automatically. This paper also develops a new metric that measures multiple attributes of clustering open set data. Multiple experiments across two author attribution data sets demonstrate the creation an incremental model that produces excellent results.Comment: Accepted to Future of Information and Communication Conference (FICC) 202

Generation of Simple Sentences in English Using the Connectionist Model of Computation

This paper discusses the design and implementation of a connectionist system for generation of well-formed English sentences of limited length and syntactic variability. The design employs several levels of interacting units for making appropriate decisions. It uses a simple technique for specifying assignment of input concepts to roles in a sentence and also has a reusable subnetwork for the expansion of noun phrases. The same NP-subnetwork is used for the expansion of noun phrases corresponding to the subject as well as the object phrases of the generated sentences.The input to the system consists of parallel activation of a cluster of nodes representing conceptual specification of the sentence whereas the output is in the form of sequential activation of nodes corresponding to the words constituting the sentence. The system can produce simple sentences in both active and passive voices, and in several tenses. Results of a simulation experiment performed are also included