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Named Entity Recognition in Tweets: An Experimental Study
TLDR
The novel T-ner system doubles F1 score compared with the Stanford NER system, and leverages the redundancy inherent in tweets to achieve this performance, using LabeledLDA to exploit Freebase dictionaries as a source of distant supervision. Expand
Deep Reinforcement Learning for Dialogue Generation
TLDR
This work simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity, non-repetitive turns, coherence, and ease of answering. Expand
Adversarial Learning for Neural Dialogue Generation
TLDR
This work applies adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances, and investigates models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Expand
SemEval-2013 Task 2: Sentiment Analysis in Twitter
TLDR
Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask. Expand
SemEval-2016 Task 4: Sentiment Analysis in Twitter
TLDR
The fourth year of the SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions, and the task continues to be very popular, attracting a total of 43 teams. Expand
Unsupervised Modeling of Twitter Conversations
TLDR
This work proposes the first unsupervised approach to the problem of modeling dialogue acts in an open domain, trained on a corpus of noisy Twitter conversations, and addresses the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. Expand
Never-Ending Learning
TLDR
The Never-Ending Language Learner is described, which achieves some of the desired properties of a never-ending learner, and lessons learned are discussed. Expand
Data-Driven Response Generation in Social Media
TLDR
It is found that mapping conversational stimuli onto responses is more difficult than translating between languages, due to the wider range of possible responses, the larger fraction of unaligned words/phrases, and the presence of large phrase pairs whose alignment cannot be further decomposed. Expand
Open domain event extraction from twitter
TLDR
TwiCal is described-- the first open-domain event-extraction and categorization system for Twitter, and a novel approach for discovering important event categories and classifying extracted events based on latent variable models is presented. Expand
Shared Tasks of the 2015 Workshop on Noisy User-generated Text: Twitter Lexical Normalization and Named Entity Recognition
TLDR
The task, annotation process and dataset statistics are outlined, and a high-level overview of the participating systems for each shared task is provided. Expand
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