# Cognitive-aware Short-text Understanding for Inferring Professions

arXiv: https://arxiv.org/abs/2106.07363 · HTML: https://mlsanity.com/paper/2106.07363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding](https://mlsanity.com/paper/1910.12180/) (2019)
- [What the Language You Tweet Says About Your Occupation](https://mlsanity.com/paper/1701.06233/) (2017)
- [TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs](https://mlsanity.com/paper/1907.03191/) (2019)
- [Leveraging Multi-aspect Time-related Influence in Location Recommendation](https://mlsanity.com/paper/1701.00595/) (2017)
- [MCP: a Multi-Component learning machine to Predict protein secondary structure](https://mlsanity.com/paper/1806.06394/) (2018)

## Abstract (arXiv)

Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications. Yet challenges abound. Firstly brief textual contents come with excessive lexical noise that makes the inference problem challenging. Secondly, cognitive-semantics are not evident, and important linguistic features are latent in short-text contents. Thirdly, it is hard to measure the correlation between the cognitive short-text semantics and the features pertaining various occupations. We argue that the multi-aspect cognitive features are needed to correctly associate short-text contents to a particular job and discover suitable people for the careers. To this end, we devise a novel framework that on the one hand, can infer short-text contents and exploit cognitive features, and on the other hand, fuses various adopted novel algorithms, such as curve fitting, support vector, and boosting modules to better predict the occupation of the authors. The final estimation module manufactures the Rw-tree via coherence weight to tune the best outcome in the inferring process. We conduct comprehensive experiments on real-life Twitter data. The experimental results show that compared to other rivals, our cognitive multi-aspect model can achieve a higher performance in the career estimation procedure, where it is inevitable to neglect the contextual semantics of users.