arxiv: 2604.21885 · v1 · submitted 2026-04-23 · 💻 cs.CL · cs.AI

Recognition: unknown

A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents

Praval Sharma

Authors on Pith no claims yet

Pith reviewed 2026-05-09 21:46 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords event extractionopen-domainmultimodalgraph-based learninglarge language modelsdocument reasoningnatural language processing

0 comments

The pith

MODEE combines graph-based learning with LLM text representations to outperform state-of-the-art open-domain event extraction and generalize to closed-domain tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Event extraction from documents supports key applications like summarization and emergency decision-making but faces limits in both predefined-type closed-domain systems and open-domain systems that ignore LLMs or full document context. This paper introduces MODEE to fix those gaps by pairing graph structures for relational and structural signals with LLM text embeddings for semantics. The goal is to handle unconstrained event types while explicitly modeling document-level reasoning that LLMs alone struggle with due to context loss and diluted attention. Large-scale tests show MODEE beats prior open-domain methods and also exceeds existing closed-domain algorithms when applied there. A sympathetic reader cares because accurate, flexible event extraction can turn raw long documents into usable structured knowledge for downstream AI systems.

Core claim

MODEE is a multimodal approach for open-domain event extraction that integrates graph-based learning with text representations from large language models to model document-level contextual, structural, and semantic reasoning. This design directly targets limitations of closed-domain methods restricted to fixed event types and open-domain methods that overlook LLMs or fail to address lost-in-the-middle and attention dilution effects. Empirical evaluations on large datasets establish that MODEE surpasses state-of-the-art open-domain baselines and, when generalized, also outperforms existing closed-domain algorithms.

What carries the argument

MODEE, the multimodal framework that fuses graph-based learning for document structure with LLM-derived text representations to capture full contextual and semantic signals.

If this is right

MODEE enables more reliable event extraction for document summarization and emergency response decision-making.
The same multimodal design generalizes directly to closed-domain event extraction and beats existing specialized algorithms there.
Explicit graph modeling mitigates specific LLM weaknesses in long-document reasoning for extraction tasks.
The approach supports unconstrained event types without requiring predefined schemas.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Hybrid graph-LLM designs may transfer to other long-document tasks such as relation extraction or multi-hop question answering.
Ablation studies isolating the graph component could clarify how much of the gain comes from structure versus LLM semantics.
The method suggests a general template for compensating LLM context limitations through explicit relational graphs in information extraction.

Load-bearing premise

That combining graph-based learning with LLM text representations will successfully model document-level contextual, structural, and semantic reasoning and overcome the lost-in-the-middle phenomenon and attention dilution in LLMs.

What would settle it

A new large-scale benchmark evaluation in which MODEE fails to produce higher F1 or similar metrics than the strongest prior open-domain event extraction baseline would falsify the performance claim.

Figures

Figures reproduced from arXiv: 2604.21885 by Praval Sharma.

**Figure 1.** Figure 1: Overview of event extraction in MODEE. 3.2.4 Attention-Based Gated Multimodal Fusion This module in MODEE integrates token-level contextual embeddings from the text encoder with document-level structure- and semantic-aware node-level representations from the graph encoder to produce rich integrated multimodal embeddings. The integration process involves two steps: Attention-based gating vector computation… view at source ↗

**Figure 2.** Figure 2: Example of a document from our dataset with the 5Ws for the main event annotated (highlighted). [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Figure 3: One-shot prompt example [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

**Figure 4.** Figure 4: One-shot prompt example [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

read the original abstract

Event extraction is essential for event understanding and analysis. It supports tasks such as document summarization and decision-making in emergency scenarios. However, existing event extraction approaches have limitations: (1) closed-domain algorithms are restricted to predefined event types and thus rarely generalize to unseen types and (2) open-domain event extraction algorithms, capable of handling unconstrained event types, have largely overlooked the potential of large language models (LLMs) despite their advanced abilities. Additionally, they do not explicitly model document-level contextual, structural, and semantic reasoning, which are crucial for effective event extraction but remain challenging for LLMs due to lost-in-the-middle phenomenon and attention dilution. To address these limitations, we propose multimodal open-domain event extraction, MODEE , a novel approach for open-domain event extraction that combines graph-based learning with text-based representation from LLMs to model document-level reasoning. Empirical evaluations on large datasets demonstrate that MODEE outperforms state-of-the-art open-domain event extraction approaches and can be generalized to closed-domain event extraction, where it outperforms existing algorithms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MODEE fuses graphs with LLM reps to tackle document-level event extraction, but the abstract's performance claims can't be checked without methods or numbers.

read the letter

The paper's main contribution is MODEE, which combines graph-based learning with LLM text representations to do open-domain event extraction while trying to capture document structure and semantics that pure LLMs miss. It points out real issues like closed-domain restrictions and the lost-in-the-middle problem in long documents, then claims the hybrid approach fixes them enough to beat existing methods and even generalize to closed-domain tasks. That framing is straightforward and hits on a practical gap in current event extraction work. The idea itself is reasonable: graphs can explicitly encode relations and structure that attention mechanisms dilute over long text. If the full paper shows a clean way to build those graphs from documents and fuse them without adding too much complexity, it could be a useful incremental step for people building document understanding pipelines. The soft spots are in the evidence. The abstract asserts outperformance on large datasets and generalization but gives no metrics, baselines, ablation results, or even a sketch of how the graphs are constructed or how the fusion layer works. Without that, it's hard to know whether any gains actually come from better reasoning or from other factors like dataset choice or a stronger backbone model. The stress-test concern lands: the mechanism for countering attention dilution is asserted but not demonstrated in the available text. This is for NLP folks working on event extraction, summarization, or hybrid graph-LLM models who want to see if the combination moves the needle on long-document tasks. It deserves peer review so the methods and results can be examined properly; the core idea is clear enough that referees could give useful feedback on whether the experiments close the gap.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes MODEE, a novel multimodal approach for open-domain event extraction that combines graph-based learning with text representations from large language models (LLMs) to model document-level contextual, structural, and semantic reasoning. It claims to overcome limitations of existing closed-domain and open-domain methods, particularly the lost-in-the-middle phenomenon and attention dilution in LLMs. The abstract states that empirical evaluations on large datasets show MODEE outperforming state-of-the-art open-domain event extraction approaches and generalizing to closed-domain event extraction where it also outperforms existing algorithms.

Significance. Should the empirical results be substantiated, this approach could represent a meaningful advance in event extraction by explicitly incorporating structural information via graphs to complement LLM strengths, potentially enabling better handling of long documents and generalization to unseen event types. The work builds on established graph and LLM techniques, and if the fusion mechanism is effective, it addresses a recognized challenge in LLM-based document understanding.

major comments (2)

[Abstract] The assertion that 'Empirical evaluations on large datasets demonstrate that MODEE outperforms state-of-the-art...' lacks any supporting metrics, baseline comparisons, dataset specifications, ablation studies, or experimental setup details. This absence prevents verification of the central empirical claim and is load-bearing for the paper's contribution.
[Abstract] The description of the MODEE approach does not specify the graph construction process (e.g., what constitutes nodes and edges, how document structure is encoded), the multimodal fusion architecture, or any mechanism by which the graph+LLM combination specifically mitigates lost-in-the-middle and attention dilution. Without these, it is unclear if the claimed reasoning improvement is achieved or if outperformance stems from other factors.

minor comments (1)

[Abstract] The term 'multimodal' is used, but the approach is described as combining text and graph modalities; consider clarifying if additional modalities are involved or if 'multimodal' refers specifically to this text-graph fusion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and will revise the abstract accordingly to improve substantiation and clarity while preserving its concise nature.

read point-by-point responses

Referee: [Abstract] The assertion that 'Empirical evaluations on large datasets demonstrate that MODEE outperforms state-of-the-art...' lacks any supporting metrics, baseline comparisons, dataset specifications, ablation studies, or experimental setup details. This absence prevents verification of the central empirical claim and is load-bearing for the paper's contribution.

Authors: We agree that the abstract, being a high-level summary, does not embed the full quantitative details. The manuscript's Experiments section provides the complete substantiation, including specific performance metrics on large datasets, comparisons against state-of-the-art baselines, dataset specifications, ablation studies, and experimental setup. To directly address this point, we will revise the abstract to include concise references to key results (e.g., relative improvements over baselines) and the primary datasets used, thereby strengthening the empirical claim at the summary level. revision: yes
Referee: [Abstract] The description of the MODEE approach does not specify the graph construction process (e.g., what constitutes nodes and edges, how document structure is encoded), the multimodal fusion architecture, or any mechanism by which the graph+LLM combination specifically mitigates lost-in-the-middle and attention dilution. Without these, it is unclear if the claimed reasoning improvement is achieved or if outperformance stems from other factors.

Authors: The abstract offers a concise overview of the multimodal approach. Detailed specifications—including graph construction (nodes as document entities/mentions with edges encoding syntactic dependencies, semantic relations, and structural document links), the multimodal fusion architecture (combining GNN-derived graph embeddings with LLM text representations via cross-attention), and the explicit mitigation of lost-in-the-middle and attention dilution through graph-based long-range dependency modeling—are provided in the Methodology and Model sections. We will revise the abstract to briefly note the graph construction process and fusion mechanism to clarify how these elements contribute to the claimed improvements. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical proposal combines independent established techniques

full rationale

The paper proposes MODEE as a multimodal combination of graph-based learning and LLM text representations for open-domain event extraction, with central claims resting on empirical outperformance evaluations rather than any derivation that reduces to its own inputs. No equations, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations appear in the abstract or described approach. The method is presented as an integration of two pre-existing techniques (graphs and LLMs) to address stated LLM limitations, without smuggling ansatzes or renaming known results as novel derivations. The derivation chain is self-contained as a novel architecture definition plus external dataset testing.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

Ledger is constructed solely from statements in the abstract because the full manuscript text was not available for review.

axioms (2)

domain assumption Large language models suffer from the lost-in-the-middle phenomenon and attention dilution when processing long documents.
Explicitly stated in the abstract as a core limitation of LLMs for document-level reasoning.
domain assumption Graph-based learning can capture document-level contextual, structural, and semantic information that LLMs miss.
Implicit premise underlying the multimodal design of MODEE.

invented entities (1)

MODEE no independent evidence
purpose: Multimodal open-domain event extraction system
New named method introduced by the paper that integrates graphs and LLMs.

pith-pipeline@v0.9.0 · 5474 in / 1378 out tokens · 46986 ms · 2026-05-09T21:46:56.353394+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

279 extracted references · 192 canonical work pages · 1 internal anchor

[1]

Standardization of. United. 1972 , file =. doi:10.18356/93a859b6-en , abstract =

work page doi:10.18356/93a859b6-en 1972
[2]

Computers, environment and urban systems , author =

Constructing gazetteers from volunteered. Computers, environment and urban systems , author =. 2017 , note =. doi:10.1016/j.compenvurbsys.2014.02.004 , abstract =

work page doi:10.1016/j.compenvurbsys.2014.02.004 2017
[3]

Enhancing

Zhang, Longyin and Zou, Bowei and Aw, AiTi , editor =. Enhancing. Proceedings of the 63rd. 2025 , pages =. doi:10.18653/v1/2025.acl-long.801 , abstract =

work page doi:10.18653/v1/2025.acl-long.801 2025
[4]

Lin, Chin-Yew , month = jul, year =. Text
[5]

BERTScore: Evaluating Text Generation with BERT

Zhang, Tianyi and Kishore, Varsha and Wu, Felix and Weinberger, Kilian Q. and Artzi, Yoav , month = feb, year =. doi:10.48550/arXiv.1904.09675 , abstract =

work page internal anchor Pith review doi:10.48550/arxiv.1904.09675 1904
[6]

and Fei, Hao , month = dec, year =

Li, Bobo and Han, Xudong and Liu, Jiang and Ding, Yuzhe and Jing, Liqiang and Zhang, Zhaoqi and Li, Jinheng and Du, Xinya and Li, Fei and Zhang, Meishan and Zhang, Min and Sun, Aixin and Yu, Philip S. and Fei, Hao , month = dec, year =. Event. doi:10.48550/arXiv.2512.19537 , abstract =

work page doi:10.48550/arxiv.2512.19537
[7]

Identifying the

Choubey, Prafulla Kumar and Raju, Kaushik and Huang, Ruihong , editor =. Identifying the. Proceedings of the 2018. 2018 , pages =. doi:10.18653/v1/N18-2055 , abstract =

work page doi:10.18653/v1/n18-2055 2018
[8]

Upadhyay, Shyam and Christodoulopoulos, Christos and Roth, Dan , editor =. “. Proceedings of the. 2016 , pages =. doi:10.18653/v1/W16-1001 , urldate =

work page doi:10.18653/v1/w16-1001 2016
[9]

IEEE transactions on knowledge and data engineering , author =

Spatially. IEEE transactions on knowledge and data engineering , author =. 2014 , note =. doi:10.1109/TKDE.2013.42 , abstract =

work page doi:10.1109/tkde.2013.42 2014
[10]

and Samet, Hanan and Sankaranarayanan, Jagan , year =

Lieberman, Michael D. and Samet, Hanan and Sankaranarayanan, Jagan , year =. Geotagging with local lexicons to build indexes for textually-specified spatial data , isbn =. doi:10.1109/ICDE.2010.5447903 , abstract =

work page doi:10.1109/icde.2010.5447903 2010
[11]

Social text , author =

Provincializing the. Social text , author =. 2004 , note =. doi:10.1215/01642472-22-4_81-65 , language =

work page doi:10.1215/01642472-22-4_81-65 2004
[12]

, year =

Hancock, Mary E. , year =. The
[13]

Cardoso, Ana Bárbara and Martins, Bruno and Estima, Jacinto , year =. Using. Progress in. doi:10.1007/978-3-030-30244-3_63 , note =

work page doi:10.1007/978-3-030-30244-3_63
[14]

Gritta, Milan and Pilehvar, Mohammad Taher and Collier, Nigel , month = jul, year =. Which. Proceedings of the 56th. doi:10.18653/v1/P18-1119 , abstract =

work page doi:10.18653/v1/p18-1119
[15]

Gazetteer-independent toponym resolution using geographic word profiles , isbn =

DeLozier, Grant and Baldridge, Jason and London, Loretta , month = jan, year =. Gazetteer-independent toponym resolution using geographic word profiles , isbn =. Proceedings of the
[16]

Annals of the Association of American Geographers , author =

Language and the. Annals of the Association of American Geographers , author =. 1991 , note =. doi:10.1111/j.1467-8306.1991.tb01715.x , abstract =

work page doi:10.1111/j.1467-8306.1991.tb01715.x 1991
[17]

Progress in human geography , author =

Geographies of toponymic inscription: new directions in critical place-name studies , volume =. Progress in human geography , author =. 2010 , note =. doi:10.1177/0309132509351042 , abstract =

work page doi:10.1177/0309132509351042 2010
[18]

International Journal of Geographical Information Science , year =

Extracting and analyzing semantic relatedness between cities using news articles , volume =. International journal of geographical information science : IJGIS , author =. 2017 , note =. doi:10.1080/13658816.2017.1367797 , abstract =

work page doi:10.1080/13658816.2017.1367797 2017
[19]

Physica A , author =

Analysis of co-occurrence toponyms in web pages based on complex networks , volume =. Physica A , author =. 2017 , note =. doi:10.1016/j.physa.2016.09.024 , abstract =

work page doi:10.1016/j.physa.2016.09.024 2017
[20]

Journal of the Royal Statistical Society

Modelling. Journal of the Royal Statistical Society. Series B (Methodological) , author =. 1977 , note =

1977
[21]

Space and place: the perspective of experience , isbn =

Tuan, Yi-fu , year =. Space and place: the perspective of experience , isbn =
[22]

Cultural anthropology , author =

". Cultural anthropology , author =. 1988 , note =. doi:10.1525/can.1988.3.2.02a00010 , abstract =

work page doi:10.1525/can.1988.3.2.02a00010 1988
[23]

History in Africa , author =

Place. History in Africa , author =. 1992 , note =. doi:10.2307/3171995 , abstract =

work page doi:10.2307/3171995 1992
[24]

Conservation biology , author =

The. Conservation biology , author =. 2002 , note =. doi:10.1046/j.1523-1739.2002.01202.x , abstract =

work page doi:10.1046/j.1523-1739.2002.01202.x 2002
[25]

Web-a-where: geotagging web content , isbn =

Amitay, Einat and Har'El, Nadav and Sivan, Ron and Soffer, Aya , year =. Web-a-where: geotagging web content , isbn =. doi:10.1145/1008992.1009040 , abstract =

work page doi:10.1145/1008992.1009040
[26]

GIS and Libraries: Patrons, Maps and Spatial Information , author =

Geographic information retrieval and spatial browsing , issn =. GIS and Libraries: Patrons, Maps and Spatial Information , author =. 1996 , note =

1996
[27]

International journal of geographical information science : IJGIS , author =

The design and implementation of. International journal of geographical information science : IJGIS , author =. 2007 , note =. doi:10.1080/13658810601169840 , abstract =

work page doi:10.1080/13658810601169840 2007
[28]

doi:10.1145/1463434.1463458 , abstract =

Teitler, Benjamin and Lieberman, Michael and Panozzo, Daniele and Sankaranarayanan, Jagan and Samet, Hanan and Sperling, Jon , year =. doi:10.1145/1463434.1463458 , abstract =

work page doi:10.1145/1463434.1463458
[29]

Philosophical transactions of the Royal Society of London

Use of the. Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences , author =. 2010 , note =. doi:10.1098/rsta.2010.0149 , abstract =

work page doi:10.1098/rsta.2010.0149 2010
[30]

Estimating

Liu, Ji and Inkpen, Diana , year =. Estimating. Proceedings of the 1st. doi:10.3115/v1/W15-1527 , abstract =

work page doi:10.3115/v1/w15-1527
[31]

Huang, Binxuan and Carley, Kathleen , month = nov, year =. A. Proceedings of the 2019. doi:10.18653/v1/D19-1480 , abstract =

work page doi:10.18653/v1/d19-1480 2019
[32]

Evaluation of georeferencing , isbn =

Tobin, Richard and Grover, Claire and Byrne, Kate and Reid, James and Walsh, Jo , year =. Evaluation of georeferencing , isbn =. Proceedings of the 6th. doi:10.1145/1722080.1722089 , abstract =

work page doi:10.1145/1722080.1722089
[33]

, year =

Radford, Benjamin J. , year =. Regressing. Proceedings of the 4th. doi:10.18653/v1/2021.case-1.8 , abstract =

work page doi:10.18653/v1/2021.case-1.8 2021
[34]

, year =

Dixon, Philip M. , year =. Ripley's. Encyclopedia of. doi:10.1002/9780470057339.var046 , note =

work page doi:10.1002/9780470057339.var046
[35]

Biometrika , author =

Notes on continuous stochastic phenomena , volume =. Biometrika , author =. 1950 , note =. doi:10.1093/biomet/37.1-2.17 , language =

work page doi:10.1093/biomet/37.1-2.17 1950
[36]

, year =

Ripley, Brian D. , year =. Statistical inference for spatial processes , isbn =
[37]

Campello, Ricardo J. G. B. and Moulavi, Davoud and Sander, Joerg , year =. Density-. Advances in. doi:10.1007/978-3-642-37456-2_14 , note =

work page doi:10.1007/978-3-642-37456-2_14
[38]

Soviet Physics Doklady , author =

Binary. Soviet Physics Doklady , author =. 1966 , note =

1966
[39]

Dictionary of

Black, Paul , year =. Dictionary of. doi:10.18434/T4/1422485 , abstract =

work page doi:10.18434/t4/1422485
[40]

A density-based algorithm for discovering clusters in large spatial databases with noise , abstract =

Ester, Martin and Kriegel, Hans-Peter and Sander, Jörg and Xu, Xiaowei , month = aug, year =. A density-based algorithm for discovering clusters in large spatial databases with noise , abstract =. Proceedings of the
[41]

International journal of humanities and arts computing , author =

Adapting the. International journal of humanities and arts computing , author =. 2015 , note =. doi:10.3366/ijhac.2015.0136 , abstract =

work page doi:10.3366/ijhac.2015.0136 2015
[42]

Journal of open source software , author =

Mordecai:. Journal of open source software , author =. 2017 , note =. doi:10.21105/joss.00091 , language =

work page doi:10.21105/joss.00091 2017
[43]

Transactions in GIS , author =

Enhancing spatial and textual analysis with. Transactions in GIS , author =. 2019 , note =. doi:10.1111/tgis.12579 , abstract =

work page doi:10.1111/tgis.12579 2019
[44]

2022 , note =

International Journal of Geographical Information Science , author =. 2022 , note =. doi:10.1080/13658816.2021.1947507 , abstract =

work page doi:10.1080/13658816.2021.1947507 2022
[45]

GeoInformatica , author =

A spatially-aware algorithm for location extraction from structured documents , issn =. GeoInformatica , author =. 2022 , keywords =. doi:10.1007/s10707-022-00482-1 , abstract =

work page doi:10.1007/s10707-022-00482-1 2022
[46]

Kamalloo, Ehsan and Rafiei, Davood , month = apr, year =. A. Proceedings of the 2018. doi:10.1145/3178876.3186027 , abstract =

work page doi:10.1145/3178876.3186027 2018
[47]

Transactions in GIS , author =

Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics , volume =. Transactions in GIS , author =. 2016 , note =. doi:10.1111/tgis.12232 , abstract =

work page doi:10.1111/tgis.12232 2016
[48]

Computers, Environment and Urban Systems , author =

A quantitative analysis of global gazetteers:. Computers, Environment and Urban Systems , author =. 2017 , keywords =. doi:10.1016/j.compenvurbsys.2017.03.007 , abstract =

work page doi:10.1016/j.compenvurbsys.2017.03.007 2017
[49]

, year =

Abrams, Lesley and Parsons, David N. , year =. Place-. Land,
[50]

Middle European Scientific Bulletin , author =

Toponymics - a. Middle European Scientific Bulletin , author =. 2021 , file =

2021
[51]

Annals of the American Association of Geographers , author =

Understanding. Annals of the American Association of Geographers , author =. 2020 , note =. doi:10.1080/24694452.2019.1694403 , abstract =

work page doi:10.1080/24694452.2019.1694403 2020
[52]

International Journal of Geographical Information Science 33, 368–384

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements , volume =. International Journal of Geographical Information Science , author =. 2019 , note =. doi:10.1080/13658816.2018.1458986 , abstract =

work page doi:10.1080/13658816.2018.1458986 2019
[53]

Language Resources and Evaluation , author =

A pragmatic guide to geoparsing evaluation , volume =. Language Resources and Evaluation , author =. 2020 , note =. doi:10.1007/s10579-019-09475-3 , abstract =

work page doi:10.1007/s10579-019-09475-3 2020
[54]

Things and

Ju, Yiting and Adams, Benjamin and Janowicz, Krzysztof and Hu, Yingjie and Yan, Bo and McKenzie, Grant , editor =. Things and. Knowledge. 2016 , keywords =. doi:10.1007/978-3-319-49004-5_23 , abstract =

work page doi:10.1007/978-3-319-49004-5_23 2016
[55]

GeoInformatica , author =

Clustering-based disambiguation of fine-grained place names from descriptions , volume =. GeoInformatica , author =. 2019 , note =. doi:10.1007/s10707-019-00341-6 , abstract =

work page doi:10.1007/s10707-019-00341-6 2019
[56]

Geocoding for texts with fine-grain toponyms: an experiment on a geoparsed hiking descriptions corpus , isbn =

Moncla, Ludovic and Renteria-Agualimpia, Walter and Nogueras-Iso, Javier and Gaio, Mauro , month = nov, year =. Geocoding for texts with fine-grain toponyms: an experiment on a geoparsed hiking descriptions corpus , isbn =. Proceedings of the 22nd. doi:10.1145/2666310.2666386 , abstract =

work page doi:10.1145/2666310.2666386
[57]

Improving

Habib, Mena Badieh and Keulen, Maurice van , month = oct, year =. Improving. Proceedings of the 4th
[58]

ISPRS International Journal of Geo-Information , author =

A. ISPRS International Journal of Geo-Information , author =. 2018 , note =. doi:10.3390/ijgi7060221 , abstract =

work page doi:10.3390/ijgi7060221 2018
[59]

Automatic

Liu, Fei and Vasardani, Maria and Baldwin, Timothy , month = nov, year =. Automatic. Proceedings of the 4th. doi:10.1145/2663713.2664426 , abstract =

work page doi:10.1145/2663713.2664426
[60]

2006 , note =

Geographic Information Sciences , author =. 2006 , note =. doi:10.1080/10824000609480611 , abstract =

work page doi:10.1080/10824000609480611 2006
[61]

Transactions of the Institute of British Geographers , author =

Sinification of. Transactions of the Institute of British Geographers , author =. 2012 , note =. doi:10.1111/j.1475-5661.2011.00471.x , abstract =

work page doi:10.1111/j.1475-5661.2011.00471.x 2012
[62]

A., and Gaffney, D

An. The Professional Geographer , author =. 2015 , note =. doi:10.1080/00330124.2014.968834 , abstract =

work page doi:10.1080/00330124.2014.968834 2015
[63]

Annals of GIS , author =

An exploratory study of place-names in. Annals of GIS , author =. 2018 , note =. doi:10.1080/19475683.2018.1501759 , abstract =

work page doi:10.1080/19475683.2018.1501759 2018
[64]

ACM Transactions on Spatial Algorithms and Systems , author =

Location. ACM Transactions on Spatial Algorithms and Systems , author =. 2016 , note =. doi:10.1145/2894745 , abstract =

work page doi:10.1145/2894745 2016
[65]

ACM Transactions on Information Systems , author =

Location. ACM Transactions on Information Systems , author =. 2018 , note =. doi:10.1145/3202662 , abstract =

work page doi:10.1145/3202662 2018
[66]

ACM Transactions on Information Systems , author =

Exploiting. ACM Transactions on Information Systems , author =. 2018 , note =. doi:10.1145/3156667 , abstract =

work page doi:10.1145/3156667 2018
[67]

ACM Transactions on Information Systems , author =

Fine-grained. ACM Transactions on Information Systems , author =. 2019 , note =. doi:10.1145/3291059 , abstract =

work page doi:10.1145/3291059 2019
[68]

Kulkarni, Sayali and Jain, Shailee and Hosseini, Mohammad Javad and Baldridge, Jason and Ie, Eugene and Zhang, Li , month = aug, year =. Multi-. Proceedings of. doi:10.18653/v1/2021.splurobonlp-1.9 , abstract =

work page doi:10.18653/v1/2021.splurobonlp-1.9 2021
[69]

Incorporating

Finkel, Jenny Rose and Grenager, Trond and Manning, Christopher , month = jun, year =. Incorporating. Proceedings of the 43rd. doi:10.3115/1219840.1219885 , urldate =

work page doi:10.3115/1219840.1219885
[70]

ISPRS International Journal of Geo-Information , author =

Deep. ISPRS International Journal of Geo-Information , author =. 2021 , note =. doi:10.3390/ijgi10120818 , abstract =

work page doi:10.3390/ijgi10120818 2021
[71]

ISPRS International Journal of Geo-Information , author =

A. ISPRS International Journal of Geo-Information , author =. 2022 , note =. doi:10.3390/ijgi11010028 , abstract =

work page doi:10.3390/ijgi11010028 2022
[72]

An empirical study on the names of points of interest and their changes with geographic distance , url =

Hu, Yingjie and Janowicz, Krzysztof , month = aug, year =. An empirical study on the names of points of interest and their changes with geographic distance , url =. Proceedings of the 10th
[73]

Understanding the bias of call detail records in human mobility research

Reconstruction of itineraries from annotated text with an informed spanning tree algorithm , volume =. International Journal of Geographical Information Science , author =. 2016 , note =. doi:10.1080/13658816.2015.1108422 , abstract =

work page doi:10.1080/13658816.2015.1108422 2016
[74]

doi:10.1080/13658816.2022.2041643 , shorttitle =

Transformer based named entity recognition for place name extraction from unstructured text , volume =. International Journal of Geographical Information Science , author =. 2023 , note =. doi:10.1080/13658816.2022.2133125 , abstract =

work page doi:10.1080/13658816.2022.2133125 2023
[75]

Computers, Environment and Urban Systems , author =

Address standardization using the natural language process for improving geocoding results , volume =. Computers, Environment and Urban Systems , author =. 2018 , keywords =. doi:10.1016/j.compenvurbsys.2018.01.009 , abstract =

work page doi:10.1016/j.compenvurbsys.2018.01.009 2018
[76]

International Journal of Geographical Information Science , author =

Comparative evaluation and analysis of online geocoding services , volume =. International Journal of Geographical Information Science , author =. 2010 , note =. doi:10.1080/13658810903289478 , language =

work page doi:10.1080/13658810903289478 2010
[77]

The Hindu , month = jun, year =

New schemes to boost growth prospects , issn =. The Hindu , month = jun, year =
[78]

Acta geographica Slovenica , author =

Exonyms and other geographical names , volume =. Acta geographica Slovenica , author =. 2017 , note =. doi:10.3986/AGS.4891 , abstract =

work page doi:10.3986/ags.4891 2017
[79]

and Abdelmoty, Alia I

Jones, Christopher B. and Abdelmoty, Alia I. and Finch, David and Fu, Gaihua and Vaid, Subodh , editor =. The. Geographic. 2004 , keywords =. doi:10.1007/978-3-540-30231-5_9 , abstract =

work page doi:10.1007/978-3-540-30231-5_9 2004
[80]

Kapur, Anu , month = mar, year =. Mapping

Showing first 80 references.