# LOME: Large Ontology Multilingual Extraction

arXiv: https://arxiv.org/abs/2101.12175 · HTML: https://mlsanity.com/paper/2101.12175/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 20 of 20)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://mlsanity.com/paper/1909.03065/) (2019)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [Syntactic Scaffolds for Semantic Structures](https://mlsanity.com/paper/1808.10485/) (2018)
- [A Structured Learning Approach to Temporal Relation Extraction](https://mlsanity.com/paper/1906.04943/) (2019)
- [Learning to Denoise Distantly-Labeled Data for Entity Typing](https://mlsanity.com/paper/1905.01566/) (2019)
- [Fine-Grained Temporal Relation Extraction](https://mlsanity.com/paper/1902.01390/) (2019)
- [Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference](https://mlsanity.com/paper/2012.08790/) (2020)
- [Hierarchical Entity Typing via Multi-level Learning to Rank](https://mlsanity.com/paper/2004.02286/) (2020)
- [Learning Joint Semantic Parsers from Disjoint Data](https://mlsanity.com/paper/1804.05990/) (2018)
- [A Broad-Coverage Deep Semantic Lexicon for Verbs](https://mlsanity.com/paper/2007.02670/) (2020)

## Built on by (most cited first, 23 of 23)

- [Moving on from OntoNotes: Coreference Resolution Model Transfer](https://mlsanity.com/paper/2104.08457/) (2021)
- [Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction](https://mlsanity.com/paper/2109.06798/) (2021)
- [Iterative Document-level Information Extraction via Imitation Learning](https://mlsanity.com/paper/2210.06600/) (2022)
- [MegaWika: Millions of reports and their sources across 50 diverse languages](https://mlsanity.com/paper/2307.07049/) (2023)
- [An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback](https://mlsanity.com/paper/2311.11226/) (2023)
- [SOCIOFILLMORE: A Tool for Discovering Perspectives](https://mlsanity.com/paper/2203.03438/) (2022)
- [Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search](https://mlsanity.com/paper/2305.10561/) (2023)
- [How ChatGPT Changed the Media's Narratives on AI: A Semi-Automated Narrative Analysis Through Frame Semantics](https://mlsanity.com/paper/2408.06120/) (2024)
- [Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports](https://mlsanity.com/paper/2209.12030/) (2022)
- [MultiMUC: Multilingual Template Filling on MUC-4](https://mlsanity.com/paper/2401.16209/) (2024)
- [InFillmore: Frame-Guided Language Generation with Bidirectional Context](https://mlsanity.com/paper/2103.04941/) (2021)
- [Mining Logical Event Schemas From Pre-Trained Language Models](https://mlsanity.com/paper/2204.05939/) (2022)
- [FAMuS: Frames Across Multiple Sources](https://mlsanity.com/paper/2311.05601/) (2023)
- [Event-Keyed Summarization](https://mlsanity.com/paper/2402.06973/) (2024)
- [Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence](https://mlsanity.com/paper/2510.26969/) (2025)
- [Human Schema Curation via Causal Association Rule Mining](https://mlsanity.com/paper/2104.08811/) (2021)
- [Kicktionary-LOME: A Domain-Specific Multilingual Frame Semantic Parsing Model for Football Language](https://mlsanity.com/paper/2108.05575/) (2021)
- [RevUp: Revise and Update Information Bottleneck for Event Representation](https://mlsanity.com/paper/2205.12248/) (2022)
- [Cross-Document Event-Keyed Summarization](https://mlsanity.com/paper/2410.14795/) (2024)
- [FutureVision: A methodology for the investigation of future cognition](https://mlsanity.com/paper/2502.01597/) (2025)
- [Evaluating the Impact of LLM-Assisted Annotation in a Perspectivized Setting: the Case of FrameNet Annotation](https://mlsanity.com/paper/2510.25904/) (2025)
- [Evaluating FrameNet-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records](https://mlsanity.com/paper/2603.18124/) (2026)
- [The Multilingual FrameNet Corpus](https://mlsanity.com/paper/2608.23037/) (2026)

## Abstract (arXiv)

We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.