en_docusco_spacy Open Source Model - Free Support for English Part-of-Speech, Rhetorical Annotation, and Named Entity Recognition

En Docusco Spacy

Developed by browndw

A spaCy pipeline for English POS and rhetorical annotation, supporting named entity recognition and part-of-speech tagging tasks.

Sequence Labeling EnglishOpen Source License:MIT #Academic Text Analysis #Rhetorical Annotation #High-Precision POS Tagging

Downloads 132

Release Time : 3/23/2022

Model Overview

This model is a spaCy-based English processing pipeline primarily designed for part-of-speech tagging and named entity recognition tasks, specifically optimized for academic texts and rhetorical analysis.

Model Features

Academic Text Optimization

Specifically optimized for academic texts and rhetorical analysis, including specialized academic terminology and writing technique recognition

Multi-Task Processing

Supports both part-of-speech tagging and named entity recognition tasks simultaneously

Fine-Grained Annotation

Provides a very detailed part-of-speech tagging scheme (314 tags)

Model Capabilities

Part-of-Speech Tagging

Named Entity Recognition

Academic Text Analysis

Rhetorical Analysis

Use Cases

Academic Research

Academic Paper Analysis

Analyze rhetorical devices and writing styles in academic papers

Can identify academic terminology, writing techniques, and other information

Text Processing

Document Annotation

Add part-of-speech and named entity labels to documents

Accuracy 97.32% (POS tagging), F-score 80.41% (NER)

🚀 en_docusco_spacy

An English pipeline for part-of-speech and rhetorical tagging, offering high - accuracy token - classification.

📚 Documentation

Overview

This is an English pipeline designed for part - of - speech and rhetorical tagging. It provides a comprehensive set of features for natural language processing tasks.

Model Information

Property	Details
Model Name	`en_docusco_spacy`
Version	`1.4`
spaCy Compatibility	`>=3.7.4,<3.8.0`
Default Pipeline	`tok2vec`, `tagger`, `ner`
Components	`tok2vec`, `tagger`, `ner`
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	n/a
License	`MIT`
Author	David Brown

Label Scheme

View label scheme (314 labels for 2 components)

Component Labels

tagger APPGE, AT, AT1, BCL21, BCL22, CC, CCB, CS, CS21, CS22, CS31, CS32, CS33, CS41, CS42, CS43, CS44, CSA, CSN, CST, CSW, CSW31, CSW32, CSW33, DA, DA1, DA2, DAR, DAT, DB, DB2, DD, DD1, DD2, DDQ, DDQGE, DDQGE31, DDQGE32, DDQGE33, DDQV, DDQV31, DDQV32, DDQV33, EX, FO, FU, FW, GE, IF, II, II21, II22, II31, II32, II33, II41, II42, II43, II44, IO, IW, JJ, JJ21, JJ22, JJ31, JJ32, JJ33, JJ41, JJ42, JJ43, JJ44, JJR, JJT, JK, MC, MC1, MC121, MC122, MC2, MC221, MC222, MCMC, MD, MF, ND1, NN, NN1, NN121, NN122, NN131, NN132, NN133, NN141, NN142, NN143, NN144, NN2, NN21, NN22, NN221, NN222, NN231, NN232, NN233, NN31, NN32, NN33, NNA, NNB, NNL1, NNL2, NNO, NNO2, NNT1, NNT131, NNT132, NNT133, NNT2, NNU, NNU1, NNU2, NNU21, NNU22, NNU221, NNU222, NP, NP1, NP2, NPD1, NPD2, NPM1, NPM2, PN, PN1, PN121, PN122, PN21, PN22, PNQO, PNQS, PNQS31, PNQS32, PNQS33, PNQV, PNQV31, PNQV32, PNQV33, PNX1, PPGE, PPH1, PPHO1, PPHO2, PPHS1, PPHS2, PPIO1, PPIO2, PPIS1, PPIS2, PPX1, PPX121, PPX122, PPX2, PPX221, PPX222, PPY, RA, RA21, RA22, REX, REX21, REX22, REX41, REX42, REX43, REX44, RG, RG21, RG22, RG31, RG32, RG33, RG41, RG42, RG43, RG44, RGQ, RGQV, RGQV31, RGQV32, RGQV33, RGR, RGT, RL, RL21, RL22, RL31, RL32, RL33, RP, RPK, RR, RR21, RR22, RR31, RR32, RR33, RR41, RR42, RR43, RR44, RR51, RR52, RR53, RR54, RR55, RRQ, RRQV, RRQV31, RRQV32, RRQV33, RRR, RRT, RT, RT21, RT22, RT31, RT32, RT33, RT41, RT42, RT43, RT44, TO, UH, UH21, UH22, UH31, UH32, UH33, VB0, VBDR, VBDZ, VBG, VBI, VBM, VBN, VBR, VBZ, VD0, VDD, VDG, VDI, VDN, VDZ, VH0, VHD, VHG, VHI, VHN, VHZ, VM, VM21, VM22, VMK, VV0, VVD, VVG, VVGK, VVI, VVN, VVNK, VVZ, XX, Y, ZZ1, ZZ2, ZZ221, ZZ222

ner AcademicTerms, AcademicWritingMoves, Character, Citation, CitationAuthority, CitationHedged, ConfidenceHedged, ConfidenceHigh, ConfidenceLow, Contingent, Description, Facilitate, FirstPerson, ForceStressed, Future, InformationChange, InformationChangeNegative, InformationChangePositive, InformationExposition, InformationPlace, InformationReportVerbs, InformationStates, InformationTopics, Inquiry, Interactive, MetadiscourseCohesive, MetadiscourseInteractive, Narrative, Negative, Positive, PublicTerms, Reasoning, Responsibility, Strategic, Uncertainty, Updates

Component	Labels
`tagger`	`APPGE`, `AT`, `AT1`, `BCL21`, `BCL22`, `CC`, `CCB`, `CS`, `CS21`, `CS22`, `CS31`, `CS32`, `CS33`, `CS41`, `CS42`, `CS43`, `CS44`, `CSA`, `CSN`, `CST`, `CSW`, `CSW31`, `CSW32`, `CSW33`, `DA`, `DA1`, `DA2`, `DAR`, `DAT`, `DB`, `DB2`, `DD`, `DD1`, `DD2`, `DDQ`, `DDQGE`, `DDQGE31`, `DDQGE32`, `DDQGE33`, `DDQV`, `DDQV31`, `DDQV32`, `DDQV33`, `EX`, `FO`, `FU`, `FW`, `GE`, `IF`, `II`, `II21`, `II22`, `II31`, `II32`, `II33`, `II41`, `II42`, `II43`, `II44`, `IO`, `IW`, `JJ`, `JJ21`, `JJ22`, `JJ31`, `JJ32`, `JJ33`, `JJ41`, `JJ42`, `JJ43`, `JJ44`, `JJR`, `JJT`, `JK`, `MC`, `MC1`, `MC121`, `MC122`, `MC2`, `MC221`, `MC222`, `MCMC`, `MD`, `MF`, `ND1`, `NN`, `NN1`, `NN121`, `NN122`, `NN131`, `NN132`, `NN133`, `NN141`, `NN142`, `NN143`, `NN144`, `NN2`, `NN21`, `NN22`, `NN221`, `NN222`, `NN231`, `NN232`, `NN233`, `NN31`, `NN32`, `NN33`, `NNA`, `NNB`, `NNL1`, `NNL2`, `NNO`, `NNO2`, `NNT1`, `NNT131`, `NNT132`, `NNT133`, `NNT2`, `NNU`, `NNU1`, `NNU2`, `NNU21`, `NNU22`, `NNU221`, `NNU222`, `NP`, `NP1`, `NP2`, `NPD1`, `NPD2`, `NPM1`, `NPM2`, `PN`, `PN1`, `PN121`, `PN122`, `PN21`, `PN22`, `PNQO`, `PNQS`, `PNQS31`, `PNQS32`, `PNQS33`, `PNQV`, `PNQV31`, `PNQV32`, `PNQV33`, `PNX1`, `PPGE`, `PPH1`, `PPHO1`, `PPHO2`, `PPHS1`, `PPHS2`, `PPIO1`, `PPIO2`, `PPIS1`, `PPIS2`, `PPX1`, `PPX121`, `PPX122`, `PPX2`, `PPX221`, `PPX222`, `PPY`, `RA`, `RA21`, `RA22`, `REX`, `REX21`, `REX22`, `REX41`, `REX42`, `REX43`, `REX44`, `RG`, `RG21`, `RG22`, `RG31`, `RG32`, `RG33`, `RG41`, `RG42`, `RG43`, `RG44`, `RGQ`, `RGQV`, `RGQV31`, `RGQV32`, `RGQV33`, `RGR`, `RGT`, `RL`, `RL21`, `RL22`, `RL31`, `RL32`, `RL33`, `RP`, `RPK`, `RR`, `RR21`, `RR22`, `RR31`, `RR32`, `RR33`, `RR41`, `RR42`, `RR43`, `RR44`, `RR51`, `RR52`, `RR53`, `RR54`, `RR55`, `RRQ`, `RRQV`, `RRQV31`, `RRQV32`, `RRQV33`, `RRR`, `RRT`, `RT`, `RT21`, `RT22`, `RT31`, `RT32`, `RT33`, `RT41`, `RT42`, `RT43`, `RT44`, `TO`, `UH`, `UH21`, `UH22`, `UH31`, `UH32`, `UH33`, `VB0`, `VBDR`, `VBDZ`, `VBG`, `VBI`, `VBM`, `VBN`, `VBR`, `VBZ`, `VD0`, `VDD`, `VDG`, `VDI`, `VDN`, `VDZ`, `VH0`, `VHD`, `VHG`, `VHI`, `VHN`, `VHZ`, `VM`, `VM21`, `VM22`, `VMK`, `VV0`, `VVD`, `VVG`, `VVGK`, `VVI`, `VVN`, `VVNK`, `VVZ`, `XX`, `Y`, `ZZ1`, `ZZ2`, `ZZ221`, `ZZ222`
`ner`	`AcademicTerms`, `AcademicWritingMoves`, `Character`, `Citation`, `CitationAuthority`, `CitationHedged`, `ConfidenceHedged`, `ConfidenceHigh`, `ConfidenceLow`, `Contingent`, `Description`, `Facilitate`, `FirstPerson`, `ForceStressed`, `Future`, `InformationChange`, `InformationChangeNegative`, `InformationChangePositive`, `InformationExposition`, `InformationPlace`, `InformationReportVerbs`, `InformationStates`, `InformationTopics`, `Inquiry`, `Interactive`, `MetadiscourseCohesive`, `MetadiscourseInteractive`, `Narrative`, `Negative`, `Positive`, `PublicTerms`, `Reasoning`, `Responsibility`, `Strategic`, `Uncertainty`, `Updates`

Accuracy

Type	Score
`TAG_ACC`	97.32
`ENTS_F`	80.41
`ENTS_P`	80.00
`ENTS_R`	80.83
`TOK2VEC_LOSS`	297770148.23
`TAGGER_LOSS`	4485596.63
`NER_LOSS`	21563546.64

Model Performance Metrics

The model has been evaluated on multiple tasks:

NER:
- NER Precision: 0.7999501539
- NER Recall: 0.8082591001
- NER F Score: 0.8040831626
TAG:
- TAG (XPOS) Accuracy: 0.9732027902

📄 License

This project is licensed under the MIT license.

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