Finding the Most Probable Ranking of Objects with Probabilistic Pairwise Preferences

Parakhin, Mikhail; Haluptzok, Patrick

doi:10.1109/icdar.2009.228

Cited by 3 publications

(1 citation statement)

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“…To aggregate the pairwise comparisons, we follow a greedy algorithm proposed by Cohen et al (1998) and used for preference ranking (Parakhin and Haluptzok, 2009). For each segmentation s in the candidate set S = {s 1 , s 2 , .…”

Section: Pairwise Neural Ranking Modelmentioning

confidence: 99%

Multi-task Pairwise Neural Ranking for Hashtag Segmentation

Maddela¹,

Preoţiuc-Pietro

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. 1 Our novel neural approaches demonstrate 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6% increase in average recall on the Se-mEval 2017 sentiment analysis dataset.

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Section: Pairwise Neural Ranking Modelmentioning

confidence: 99%