Abstract

Like SemEval 2013 and 2014, the task Sentiment Analysis in Twitter found a place in this year’s SemEval too and attracted an unprecedented number of participations. This task comprises of four sub-tasks. We participated in subtask 2—Message polarity classification. Although we lie a few notches down from the top system, we present a very simple yet effective approach to handle this problem that can be implemented in a single day!

Abstract

Following the footsteps of SemEval-2014 Task 4 (Pontiki et al., 2014), SemEval-2015 too had a task dedicated to aspect-level sentiment analysis (Pontiki et al., 2015), which saw participation from over 25 teams. In Aspectbased Sentiment Analysis, the aim is to identify the aspects of entities and the sentiment expressed for each aspect. In this paper, we present a detailed description of our system, that stood 4th in Aspect Category subtask (slot 1), 7th in Opinion Target Expression subtask (slot 2) and 8th in Sentiment Polarity subtask (slot 3) on the Restaurant datasets.

Abstract

This paper describes the system that was submitted to SemEval2015 Task 10: Sentiment Analysis in Twitter. We participated in Subtask B: Message Polarity Classification. The task is a message level classification of tweets into positive, negative and neutral sentiments. Our model is primarily a supervised one which consists of well designed features fed into an SVM classifier. In previous runs of this task, it was found that lexicons played an important role in determining the sentiment of a tweet. We use existing lexicons to extract lexicon specific features. The lexicon based features are further augmented by tweet specific features. We also improve our system by using acronym and emoticon dictionaries. The proposed system achieves an F1 score of 59.83 and 67.04 on the Test Data and Progress Data respectively. This placed us at the 18th position for the Test Dataset and the 16th position for the Progress Test Dataset.

Abstract

On various microblogging platforms like Twitter, the users post short text messages ranging from news and information to thoughts and daily chatter. These messages often contain keywords called Hashtags, which are semantico-syntactic constructs that enable topical classification of the microblog posts. In this poster, we propose and evaluate a novel method of semantic enrichment of microblogs for a particular type of entity search--retrieving a ranked list of the top-k hashtags relevant to a user's query Q. Such a list can help the users track posts of their general interest. We show that our technique significantly improved microblog retrieval as well. We tested our approach on the publicly available Stanford sentiment analysis tweet corpus. We observed an improvement of more than 10% in NDCG for microblog retrieval task, and around 11% in mean average precision for hashtag retrieval task.

Abstract

Following the footsteps of SemEval-2014 Task 4 (Pontiki et al., 2014), SemEval-2015 too had a task dedicated to aspect-level sentiment analysis (Pontiki et al., 2015), which saw participation from over 25 teams. In Aspectbased Sentiment Analysis, the aim is to identify the aspects of entities and the sentiment expressed for each aspect. In this paper, we present a detailed description of our system, that stood 4th in Aspect Category subtask (slot 1), 7th in Opinion Target Expression subtask (slot 2) and 8th in Sentiment Polarity subtask (slot 3) on the Restaurant datasets.

Abstract

Extractive summarization is the process of precisely choosing a set of sentences from a corpus which can actually be a representative of the original corpus in a limited space. In addition to exhibiting a good content coverage, the final summary should be readable as well as structurally and topically coherent. In this paper we present a holistic, multi-document summarization approach which takes care of the content coverage, sentence ordering, maintenance of topical coherence, topical order and inter-sentence structural relationships. To achieve this we have introduced a novel concept of a Local Coherent Unit (LCU). Our results are comparable with the peer systems for content coverage and sentence ordering measured in terms of ROUGE and τ score respectively. The human evaluation preference for readability and coherence of summary are significantly better for our approach vis a vis other approaches. The approach is scalable to bigger realtime corpus as well.

Abstract

Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities (“NSA” and “Edward Snowden” in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag.

Abstract

Software Engineering (SE) educators worldwide are attempting to create learning environments that can effectively achieve their desired learning objectives. However, there exist additional needs that impact the learning process and the overall quality of a learning environment. We identified two sets of differentiating requirements, Climatic and Systemic, whose inclusion in design can assist in an effective, sustainable and usable SE learning environment. In this paper, we will describe the Systemic requirements, i.e. the desired system wide capabilities that impact the sustainability of a SE learning environment by affecting its operationalization and use in short and long term. We will also discuss, through few examples, the interactions between various differentiating requirements. Current SE course design and evaluation consider these as challenges to deal later, instead of addressing them through a conscientious design. Such courses find it hard to sustain and evolve with time, despite using powerful pedagogies. We intend to change this design approach by identifying and recording the various needs (as requirements) and their influence on the learning environment. Our aim is to draw attention to these differentiating requirements and help the educators look beyond learning objectives and move towards a more holistic and systematic design of SE learning environments.

Abstract

Case-based teaching is a well-known teaching methodology consisting of learning by reading, discussing and analyzing real-life cases and scenarios. We present a Case-Oriented Learning Environment (COSEEd) for teaching Software Engineering concepts to undergraduate and graduate students in a first course of Software Engineering. The novelty of the proposed model lies in being a complete learning environment framework, consisting of pedagogy, broad level learning objectives, assessment, resources and management details, all designed specifically for Software Engineering. Learning and teaching is centered around well-designed SE case studies from authentic software development instances. We describe the COSEEd model, a sample case-study and share out insights as well as lessons learnt while applying the proposed model in practice. We implement and evaluate the proposed model in Software Engineering courses at a University in India focused on the core areas of Information Technology. We use empirical studies on student perception and actual performance to determine the effectiveness of COSEEd towards achieving various learning goals of SE.

Abstract

We present here, TagMiner, a data mining approach for part-of-speech (POS) tagging, an important Natural language process-ing (NLP) classification task. It is a semi-supervised associative clas-sification method for POS tagging. Existing methods for building POS taggers require extensive domain and linguistic knowledge and resources.Our method uses combination of a small POS tagged corpus and a raw untagged text data as training data to build the classifier model using association rules. Our tagger works well with very little training dataalso. The use of semi-supervised learning provides the advantage of notrequiring a large high quality tagged corpus. These properties make it es-pecially suitable for resource poor languages. Our experiments on various resource-rich, resource-moderate and resource-poor languages show good performance without using any language specific linguistic information.We note that inclusion of such features in our method may further im-prove the performance. Results also show that for smaller training data sizes our tagger performs better than state-of-the-art CRF tagger using same features as our tagger.