Learning to Recognize Affective Polarity in Similes

A simile is a comparison between two essentially unlike things, such as “Jane swims like a dolphin”. Similes often ex-press a positive or negative sentiment to-ward something, but recognizing the po-larity of a simile can depend heavily on world knowledge. For example, “memory like an elephant ” is positive, but “mem-ory like a sieve ” is negative. Our research explores methods to recognize the polarity of similes on Twitter. We train classifiers using lexical, semantic, and sentiment fea-tures, and experiment with both manu-ally and automatically generated training data. Our approach yields good perfor-mance at identifying positive and negative similes, and substantially outperforms ex-isting sentiment resources.

Semantic Lexicon Induction from Twitter with Pattern Relatedness and Flexible Term Length

With the rise of social media, learning from informal text has become increasingly important. We present a novel semantic lexicon induction approach that is able to learn new vocabulary from social media. Our method is robust to the idiosyncrasies of informal and open-domain text corpora. Unlike previous work, it does not impose restrictions on the lexical features of candidate terms — e.g. by restricting entries to nouns or noun phrases —while still being able to accurately learn multiword phrases of variable length. Starting with a few seed terms for a semantic category, our method first explores the context around seed terms in a corpus, and identifies context patterns that are relevant to the category. These patterns are used to extract candidate terms — i.e. multiword segments that are further analyzed to ensure meaningful term boundary segmentation. We show that our approach is able to learn high quality semantic lexicons from informally written social media text of Twitter, and can achieve accuracy as high as 92% in the top 100 learned category members

Condensed Memory Networks for Clinical Diagnostic Inferencing

Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab tests etc.). In contrast, we explore the problem using free-text medical notes recorded in an electronic health record (EHR). Complex tasks like these can benefit from structured knowledge bases, but those are not scalable. We instead exploit raw text from Wikipedia as a knowledge source. Memory networks have been demonstrated to be effective in tasks which require comprehension of free-form text. They use the final iteration of the learned representation to predict probable classes. We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory. Experiments on the MIMIC-III dataset show that the proposed model outperforms other variants of memory networks to predict the most probable diagnoses given a complex clinical scenario