Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
نویسندگان
چکیده
منابع مشابه
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive an...
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Scarcity of annotated corpora for many languages is a bottleneck for training finegrained sentiment analysis models that can tag aspects and subjective phrases. We propose to exploit statistical machine translation to alleviate the need for training data by projecting annotated data in a source language to a target language such that a supervised fine-grained sentiment analysis system can be tr...
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Sentiment analysis is the problem of determining the polarity of a text with respect to a particular topic. For most applications, however, it is not only necessary to derive the polarity of a text as a whole but also to extract negative and positive utterances on a more finegrained level. Sentiment analysis systems working on the (sub-)sentence level, however, are difficult to develop since sh...
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ژورنال
عنوان ژورنال: Transactions of the Association for Computational Linguistics
سال: 2018
ISSN: 2307-387X
DOI: 10.1162/tacl_a_00002