Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank, which is proposed to measure the influence of users in Twitter.
This paper empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling, and finds interesting and useful findings for downstream IR or DM applications.
This work proposes an end-to-end neural architecture for the Stanford Question Answering Dataset (SQuAD), based on match-LSTM, a model previously proposed previously for textual entailment, and Pointer Net, a sequence- to-sequence model proposed by Vinyals et al.(2015) to constrain the output tokens to be from the input sequences.
A retrieval function (REP) to measure the similarity between two bug reports, which fully utilizes the information available in a bug report including not only the similarity of textual content in summary and description fields, but also similarity of non-textual fields such as product, component, version, etc.
A special long short-term memory (LSTM) architecture for NLI that remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label and achieves an accuracy of 86.1%, outperforming the state of the art.
This paper proposes a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words and shows that with a relatively small amount of training data, this model can effectively identify aspect and opinion words simultaneously.
A probabilistic model based on collaborative filtering and topic modeling is proposed that allows it to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on aPer-Aspect basis.
A general "compare-aggregate" framework that performs word-level matching followed by aggregation using Convolutional Neural Networks and finds that some simple comparison functions based on element-wise operations can work better than standard neural network and neural tensor network.
This paper proposes a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question, and proposes a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning.
Two models which make use of multiple passages to generate their answers using an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model are proposed.