Processing of social media text like tweets is challenging for traditional Natural Language Processing (NLP) tools developed for well-edited text due to the noisy nature of such text. However, demand for tools and resources to correctly process such noisy text has increased in recent years due to the usefulness of such text in various applications. Literature reports various efforts made to develop tools and resources to process such noisy text for various languages, notably, part-of-speech (POS) tagging, an NLP task having a direct effect on the performance of other successive text processing activities. Still, no such attempt has been made to develop a POS tagger for Urdu social media content. Thus, the focus of this paper is on POS tagging of Urdu tweets. We introduce a new tagset for POS-tagging of Urdu tweets along with the POS-tagged Urdu tweets corpus. We also investigated bootstrapping as a potential solution for overcoming the shortage of manually annotated data and present a supervised POS tagger with an accuracy of 93.8% precision, 92.9% recall and 93.3% F-measure.

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Twitter, a social media platform has experienced substantial growth over the last few years. Thus, huge number of tweets from various communities is available and used for various NLP applications such as Opinion mining, information extraction, sentiment analysis etc. One of the key pre-processing steps in such NLP applications is Part-of-Speech (POS) tagging. POS tagging of Twitter data (also called noisy text) is different than conventional POS tagging due to informal nature and presence of Twitter specific elements. Resources for POS tagging of tweet specific data are mostly available for English. Though, availability of tagset and language independent statistical taggers do provide opportunity for resource-poor languages such as Urdu to expand coverage of NLP tools to this new domain of POS tagging for which little effort has been reported. The aim of this study is twofold. First, is to investigate how well the statistical taggers developed for POS tagging of structured text fare in the domain of tweet POS tagging. Secondly, how can these taggers be used to overcome the bottleneck of manually annotated corpus for this new domain. To this end, Stanford and MorphoDiTa taggers were trained on 500 Urdu tweet gold-standard corpus and were utilized for semi-automatic corpus annotation in bootstrapped fashion. Five bootstrapping iterations for both the taggers were performed. At the end of each iteration, the performance of taggers was evaluated against the development set and automatically tagged, manually corrected 100 tweets were added in the training set to retrain both models. Finally, at the end of last iteration, tagger performance was evaluated against test set. Stanford tagger achieved an accuracy of 93.8% Precision, 92.9% Recall and 93.3% F-Measure. Whereas, MorphoDiTa tagger achieved an accuracy of 93.5% Precision, 92.6% Recall and 93% F-Measure. A thorough error analysis on the output of both taggers is also presented.

Collaborative Intelligent Tutoring Systems (ITSs) use peer tutor assessment to give feedback to students in solving problems. Through this feedback, the students reflect on their thinking and try to improve it when they get similar questions. The accuracy of the feedback given by the peers is important because this helps students to improve their learning skills. If the student acting as a peer tutor is unclear about the topic, then they will probably provide incorrect feedback. There have been very few attempts in the literature that provide limited support to improve the accuracy and relevancy of peer feedback. This paper presents a collaborative ITS to teach Unified Modeling Language (UML), which is designed in such a way that it can detect erroneous feedback before it is delivered to the student. The evaluations conducted in this study indicate that receiving and sending incorrect feedback have negative impact on students’ learning skills. Furthermore, the results also show that the experimental group with peer feedback evaluation has significant learning gains compared to the control group.