Related papers: Efficient Social Network Multilingual Classificati…
Language identification of social media text still remains a challenging task due to properties like code-mixing and inconsistent phonetic transliterations. In this paper, we present a supervised learning approach for language…
Natural language understanding often requires deep semantic knowledge. Expanding on previous proposals, we suggest that some important aspects of semantic knowledge can be modeled as a language model if done at an appropriate level of…
Emojis are being frequently used in todays digital world to express from simple to complex thoughts more than ever before. Hence, they are also being used in sentiment analysis and targeted marketing campaigns. In this work, we performed…
Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are…
Twitter, a microblogging service, has evolved into a powerful communication platform with millions of active users who generate immense volume of microposts on a daily basis. To facilitate effective categorization and easy search, users…
In this paper, we present a pressure characterization and normalization procedure for online handwritten acquisition. Normalization process has been tested in biometric recognition experiments (identification and verification) using online…
Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares…
Sentiment analysis is a very important natural language processing activity in which one identifies the polarity of a text, whether it conveys positive, negative, or neutral sentiment. Along with the growth of social media and the Internet,…
Recent studies have shown strong correlation between social networking data and national influenza rates. We expanded upon this success to develop an automated text mining system that classifies Twitter messages in real time into six…
This paper discusses the results obtained for different techniques applied for performing the sentiment analysis of social media (Twitter) code-mixed text written in Hinglish. The various stages involved in performing the sentiment analysis…
Text actionability detection is the problem of classifying user authored natural language text, according to whether it can be acted upon by a responding agent. In this paper, we propose a supervised learning framework for domain-aware,…
Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to…
In spite of their superior performance, neural probabilistic language models (NPLMs) remain far less widely used than n-gram models due to their notoriously long training times, which are measured in weeks even for moderately-sized…
Sarcasm detection identifies natural language expressions whose intended meaning is different from what is implied by its surface meaning. It finds applications in many NLP tasks such as opinion mining, sentiment analysis, etc. Today,…
Most natural language processing systems based on machine learning are not robust to domain shift. For example, a state-of-the-art syntactic dependency parser trained on Wall Street Journal sentences has an absolute drop in performance of…
In this paper, we detail novel strategies for interpolating personalized language models and methods to handle out-of-vocabulary (OOV) tokens to improve personalized language models. Using publicly available data from Reddit, we demonstrate…
This paper describes Centre for Development of Advanced Computing's (CDACM) submission to the shared task-'Tool Contest on POS tagging for Code-Mixed Indian Social Media (Facebook, Twitter, and Whatsapp) Text', collocated with ICON-2016.…
The prevalence of rapidly evolving slang, neologisms, and highly stylized expressions in informal user-generated text, particularly on Chinese social media, poses significant challenges for Machine Translation (MT) benchmarking.…
One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such…
The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of…