# Research on Intellectual Property Resource Profile and Evolution Law

arXiv: https://arxiv.org/abs/2204.06221 · HTML: https://mlsanity.com/paper/2204.06221/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 83 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Chinese NER Using Lattice LSTM](https://mlsanity.com/paper/1805.02023/) (2018)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [Self-Supervised Graph Co-Training for Session-based Recommendation](https://mlsanity.com/paper/2108.10560/) (2021)
- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval](https://mlsanity.com/paper/2203.08615/) (2022)
- [Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks](https://mlsanity.com/paper/1909.06627/) (2019)
- [GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition](https://mlsanity.com/paper/1907.05611/) (2019)
- [Multi-label Dataless Text Classification with Topic Modeling](https://mlsanity.com/paper/1711.01563/) (2017)
- [Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings](https://mlsanity.com/paper/1907.02679/) (2019)
- [Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles](https://mlsanity.com/paper/1601.00481/) (2016)

## Abstract (arXiv)

In the era of big data, intellectual property-oriented scientific and technological resources show the trend of large data scale, high information density, and low value density, which brings severe challenges to the effective use of intellectual property resources, and the demand for mining hidden information in intellectual property is increasing. This makes intellectual property-oriented science and technology resource portraits and analysis of evolution become a current research hotspot. This paper sorts out the construction method of intellectual property resource portraits and its preliminary work, including property entity extraction and entity completion, from the aspects of algorithm classification and general process, and identifies directions for improving future methods.