Alan Ritter

Open domain event extraction from twitter

Tweets are the most up-to-date and inclusive stream of in- formation and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events. Previous work on extracting structured representations of events has focused largely on newswire text; Twitters unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCal-- the first open-domain event-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain calendar of significant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models. By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar.com; Our NLP tools are available at http://github.com/aritter/ twitter_nlp.

SemEval-2014 Task 9: Sentiment Analysis in Twitter

We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year’s task that ran successfully as part of SemEval2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) LiveJournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.

SemEval-2015 Task 10: Sentiment Analysis in Twitter

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year’s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase.

SemEval-2016 Task 4: Sentiment Analysis in Twitter

This paper discusses the fourth year of the ”Sentiment Analysis in Twitter Task”. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic “sentiment classification in Twitter” task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams.

Deep Reinforcement Learning for Dialogue Generation

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model 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 (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues.

Adversarial Learning for Neural Dialogue Generation

We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning problem where we jointly train two systems: a generative model to produce response sequences, and a discriminator—analagous to the human evaluator in the Turing test— to distinguish between the human-generated dialogues and the machine-generated ones. In this generative adversarial network approach, the outputs from the discriminator are used to encourage the system towards more human-like dialogue. Further, we investigate models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines

Weakly Supervised User Profile Extraction from Twitter

While user attribute extraction on social media has received considerable attention, existing approaches, mostly supervised, encounter great difficulty in obtaining gold standard data and are therefore limited to predicting unary predicates (e.g., gender). In this paper, we present a weaklysupervised approach to user profile extraction from Twitter. Users’ profiles from social media websites such as Facebook or Google Plus are used as a distant source of supervision for extraction of their attributes from user-generated text. In addition to traditional linguistic features used in distant supervision for information extraction, our approach also takes into account network information, a unique opportunity offered by social media. We test our algorithm on three attribute domains: spouse, education and job; experimental results demonstrate our approach is able to make accurate predictions for users’ attributes based on their tweets. 1

Shared Tasks of the 2015 Workshop on Noisy User-generated Text: Twitter Lexical Normalization and Named Entity Recognition

This paper presents the results of the two shared tasks associated with W-NUT 2015: (1) a text normalization task with 10 participants; and (2) a named entity tagging task with 8 participants. We outline the task, annotation process and dataset statistics, and provide a high-level overview of the participating systems for each shared task.

Major Life Event Extraction from Twitter based on Congratulations/Condolences Speech Acts

Social media websites provide a platform for anyone to describe significant events taking place in their lives in realtime. Currently, the majority of personal news and life events are published in a textual format, motivating information extraction systems that can provide a structured representations of major life events (weddings, graduation, etc. . . ). This paper demonstrates the feasibility of accurately extracting major life events. Our system extracts a fine-grained description of users’ life events based on their published tweets. We are optimistic that our system can help Twitter users more easily grasp information from users they take interest in following and also facilitate many downstream applications, for example realtime friend recommendation.

Weakly Supervised Extraction of Computer Security Events from Twitter

Twitter contains a wealth of timely information, however staying on top of breaking events requires that an information analyst constantly scan many sources, leading to information overload. For example, a user might wish to be made aware whenever an infectious disease outbreak takes place, when a new smartphone is announced or when a distributed Denial of Service (DoS) attack might affect an organizations network connectivity. There are many possible event categories an analyst may wish to track, making it impossible to anticipate all those of interest in advance. We therefore propose a weakly supervised approach, in which extractors for new categories of events are easy to define and train, by specifying a small number of seed examples. We cast seed-based event extraction as a learning problem where only positive and unlabeled data is available. Rather than assuming unlabeled instances are negative, as is common in previous work, we propose a learning objective which regularizes the label distribution towards a user-provided expectation. Our approach greatly outperforms heuristic negatives, used in most previous work, in experiments on real-world data. Significant performance gains are also demonstrated over two novel and competitive baselines: semi-supervised EM and one-class support-vector machines. We investigate three security-related events breaking on Twitter: DoS attacks, data breaches and account hijacking. A demonstration of security events extracted by our system is available at: http://kb1.cse.ohio-state.edu:8123/events/hacked