We propose a new pooling technique for topic modeling in Twitter, which groups together tweets occurring in the same user-to-user conversation. Under this scheme, tweets and their replies are aggregated into a single document and the users who posted them are considered co-authors. To compare this new scheme against existing ones, we train topic models using Latent Dirichlet Allocation (LDA) and the Author-Topic Model (ATM) on datasets consisting of tweets pooled according to the different methods. Using the underlying categories of the tweets in this dataset as a noisy ground truth, we show that this new technique outperforms other pooling methods in terms of clustering quality and document retrieval.

Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. These machine learning systems continually learn and readjust to be able to carry out the task set out before them. Understanding how neural networks work helps researchers to develop better applications and uses for them. At the 2017 Conference on Empirical Methods on Natural Language Processing earlier this month, MIT researchers demonstrated a new general-purpose technique for making sense of neural networks that are able to carry out natural language processing tasks where they attempt to extract data written in normal text opposed to something of a structured language like database-query language. The new technique works great in any system that reads the text as input and produces symbols as the output.

Artificial-intelligence research has been transformed by machine-learning systems called neural networks, which learn how to perform tasks by analyzing huge volumes of training data. During training, a neural net continually readjusts thousands of internal parameters until it can reliably perform some task, such as identifying objects in digital images or translating text from one language to another. But on their own, the final values of those parameters say very little about how the neural net does what it does. Understanding what neural networks are doing can help researchers improve their performance and transfer their insights to other applications, and computer scientists have recently developed some clever techniques for divining the computations of particular neural networks. But, at the 2017 Conference on Empirical Methods on Natural Language Processing starting this week, researchers from MIT's Computer Science and Artificial Intelligence Laboratory are presenting a new general-purpose technique for making sense of neural networks that are trained to perform natural-language-processing tasks, in which computers attempt to interpret freeform texts written in ordinary, or "natural," language (as opposed to a structured language, such as a database-query language).