Large-Scale Goodness Polarity Lexicons for Community Question Answering

Abstract

We transfer a key idea from the field of sentiment analysis to a new domain: community question answering (cQA). e cQA task we are interested in is the following: given a question and a thread of comments, we want to re-rank the comments, so that the ones that are good answers to the question would be ranked higher than the bad ones. We notice that good vs. bad comments use specific vocabulary and that one can o en predict the goodness/badness of a comment even ignoring the question, based on the comment contents only. is leads us to the idea to build a good/bad polarity lexicon as an analogy to the positive/negative sentiment polarity lexicons, commonly used in sentiment analysis. In particular, we use pointwise mutual information in order to build large-scale goodness polarity lexicons in a semi-supervised manner starting with a small number of initial seeds. e evaluation results show an improvement of 0.7 MAP points absolute over a very strong baseline, and state-of-the art performance on SemEval-2016 Task 3.

Arabic community question answering

“…Moreover, given the recent success of deep neural networks, which largely eliminate the need for manual feature engineering, we are interested in exploring various deep neural network architectures, e.g., based on LSTMs, GRUs, CNNs, RNMs, etc., as well as various ways to generate high-quality word embeddings (Mihaylov and Nakov 2016b) and goodness polarity lexicons (Balchev et al 2016; Mihaylov et al 2017), e.g., as was done for SemEval-2016 Task 3 for English. We would also like to compare side-by-side deep neural networks to convolutional syntactic kernels, and to investigate ways to combine them.…”

Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings

“…In future work, we plan to model text complexity (Mihaylova et al, 2016), veracity (Mihaylova et al, 2018, speech act (Joty and Hoque, 2016), user profile (Mihaylov et al, 2015), trollness (Mihaylov et al, 2018), and goodness polarity Mihaylov et al, 2017). From a modeling perspective, we want to strongly couple CRF and DNN, so that the global errors are backpropagated from the CRF down to the DNN layers.…”

SemEval-2017 Task 3: Community Question Answering

“…Finally, we should mention some interesting features used by the participating systems across all three subtasks. This includes fine-tuned word embeddings 5 (Mihaylov and Nakov, 2016b); features modeling text complexity, veracity, and user trollness 6 (Mihaylova et al, 2016); sentiment polarity features (Nicosia et al, 2015); and PMI-based goodness polarity lexicons Mihaylov et al, 2017a).…”