Parinaz Sobhani

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The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we propose to extend it to tree structures, in which a memory cell can reflect the history memories of multiple child cells or multiple descendant cells in a recursive process. We call(More)
Argumentation mining and stance classification were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. Unlike other machine learning approaches for argument tagging which often require large set of labeled data, the proposed model is minimally supervised and(More)
Here for the first time we present a shared task on detecting stance from tweets: given a tweet and a target entity (person, organization , etc.), automatic natural language systems must determine whether the tweeter is in favor of the given target, against the given target, or whether neither inference is likely. The target of interest may or may not be(More)
This paper proposes neural networks for integrating compositional and non-compositional sentiment in the process of sentiment composition , a type of semantic composition that optimizes a sentiment objective. We enable individual composition operations in a recursive process to possess the capability of choosing and merging information from these two types(More)
Recurrent neural networks, particularly long short-term memory (LSTM), have recently shown to be very effective in a wide range of sequence modeling problems, core to which is effective learning of distributed representation for subsequences as well as the sequences they form. An assumption in almost all the previous models, however, posits that the learned(More)
One may express favor (or disfavor) towards a target by using positive or negative language. Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets, as well as for sentiment. These targets may or may not be referred to in the tweets, and they may or may not be the target of(More)