Implicit Dimension Identification in User-Generated Text with LSTM Networks

In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers. We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user's knowledge, intent or belief that may be based on writer's moral foundation: 1) political perspective detection in news articles 2) identification of informational vs. conversational questions in community question answering (CQA) archives and. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press

Sensing Ambiguity in Henry James' "The Turn of the Screw"

Fields such as the philosophy of language, continental philosophy, and literary studies have long established that human language is, at its essence, ambiguous and that this quality, although challenging to communication, enriches language and points to the complexity of human thought. On the other hand, in the NLP field there have been ongoing efforts aimed at disambiguation for various downstream tasks. This work brings together computational text analysis and literary analysis to demonstrate the extent to which ambiguity in certain texts plays a key role in shaping meaning and thus requires analysis rather than elimination. We revisit the discussion, well known in the humanities, about the role ambiguity plays in Henry James' 19th century novella, The Turn of the Screw. We model each of the novella's two competing interpretations as a topic and computationally demonstrate that the duality between them exists consistently throughout the work and shapes, rather than obscures, its meaning. We also demonstrate that cosine similarity and word mover's distance are sensitive enough to detect ambiguity in its most subtle literary form, despite doubts to the contrary raised by literary scholars. Our analysis is built on topic word lists and word embeddings from various sources. We first claim, and then empirically show, the interdependence between computational analysis and close reading performed by a human expert