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Mitra Mohtarami

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3 papers

1 author row

AAAI Conference 2018 Conference Paper

Fact Checking in Community Forums

Tsvetomila Mihaylova
Preslav Nakov
Lluís Màrquez
Alberto Barrón-Cedeño
Mitra Mohtarami
Georgi Karadzhov
James Glass

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author proﬁle (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86. 54, which is 21 points absolute above the baseline.

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AAAI Conference 2013 Conference Paper

From Semantic to Emotional Space in Probabilistic Sense Sentiment Analysis

Mitra Mohtarami
Man Lan
Chew Lim Tan

This paper proposes an effective approach to model the emotional space of words to infer their Sense Sentiment Similarity (SSS). SSS reflects the distance between the words regarding their senses and underlying sentiments. We propose a probabilistic approach that is built on a hidden emotional model in which the basic human emotions are considered as hidden. This leads to predict a vector of emotions for each sense of the words, and then to infer the sense sentiment similarity. The effectiveness of the proposed approach is investigated in two Natural Language Processing tasks: Indirect yes/no Question Answer Pairs Inference and Sentiment Orientation Prediction.

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AAAI Conference 2012 Conference Paper

Sense Sentiment Similarity: An Analysis

Mitra Mohtarami
Hadi Amiri
Man Lan
Thanh Phu Tran
Chew Lim Tan

This paper describes an emotion-based approach to acquire sentiment similarity of word pairs with respect to their senses. Sentiment similarity indicates the similarity between two words from their underlying sentiments. Our approach is built on a model which maps from senses of words to vectors of twelve basic emotions. The emotional vectors are used to measure the sentiment similarity of word pairs. We show the utility of measuring sentiment similarity in two main natural language processing tasks, namely, indirect yes/no question answer pairs (IQAP) Inference and sentiment orientation (SO) prediction. Extensive experiments demonstrate that our approach can effectively capture the sentiment similarity of word pairs and utilize this information to address the above mentioned tasks.

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