---
language: en
datasets: COCA
---
# docusco-bert

## Model description

**docusco-bert** is a fine-tuned BERT model that is ready to use for **token classification**. The model was trained on data sampled from the Corpus of Contemporary American English ([COCA](https://www.english-corpora.org/coca/)) and classifies tokens and token sequences according to a system developed for the [**DocuScope**](https://www.cmu.edu/dietrich/english/research-and-publications/docuscope.html) dictionary-based tagger. Descriptions of the categories are included in a table below. 

## About DocuScope
DocuScope is a dicitonary-based tagger that has been developed at Carnegie Mellon University by **David Kaufer** and **Suguru Ishizaki** since the early 2000s. Its categories are rhetorical in their orientation (as opposed to part-of-speech tags, for example, which are morphosyntactic).

DocuScope has been been used in [a wide variety of studies](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=docuscope&btnG=). Here, for example, is [a short analysis of King Lear](https://graphics.cs.wisc.edu/WP/vep/2017/02/14/guest-post-data-mining-king-lear/), and here is [a published study of Tweets](https://journals.sagepub.com/doi/full/10.1177/2055207619844865).


## Intended uses & limitations

#### How to use

The model was trained on data with tags formatted using [IOB](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)), like those used in common tasks like Named Entity Recogition (NER). Thus, you can use this model with a Transformers NER *pipeline*.

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("browndw/docusco-bert")
model = AutoModelForTokenClassification.from_pretrained("browndw/docusco-bert")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Globalization is the process of interaction and integration among people, companies, and governments worldwide."
ds_results = nlp(example)
print(ds_results)
```

#### Limitations and bias

This model is limited by its training dataset of American English texts. Moreover, the current version is trained on only a small subset of the corpus. The goal is to train later versions on more data, which should increase accuracy. 

## Training data

This model was fine-tuned on data from the Corpus of Contemporary American English ([COCA](https://www.english-corpora.org/coca/)). The training data contain chunks of text randomly sampled of 5 text-types: Academic, Fiction, Magazine, News, and Spoken.

Typically, BERT models are trained on sentence segments. However, DocuScope tags can span setences. Thus, data were split into chunks that don't split **B + I** sequences and end with sentence-final punctuation marks (i.e., period, quesiton mark or exclamaiton point).

Additionally, the order of the chunks was randomized prior to sampling, and statified sampling was used to provide enough training data for low-frequency caegories. The resulting training data consist of:

* 21,460,177 tokens
* 15,796,305 chunks

The specific counts for each category appear in the following table.

Category|Count
-|-
O|3528038
Syntactic Complexity|2032808
Character|1413771
Description|1224744
Narrative|1159201
Negative|651012
Academic Terms|620932
Interactive|594908
Information Exposition|578228
Positive|463914
Force Stressed|432631
Information Topics|394155
First Person|249744
Metadiscourse Cohesive|240822
Strategic|238255
Public Terms|234213
Reasoning|213775
Information Place|187249
Information States|173146
Information ReportVerbs|119092
Confidence High|112861
Confidence Hedged|110008
Future|96101
Inquiry|94995
Contingent|94860
Information Change|89063
Metadiscourse Interactive|84033
Updates|81424
Citation|71241
Facilitate|50451
Uncertainty|35644
Academic WritingMoves|29352
Information ChangePositive|28475
Responsibility|25362
Citation Authority|22414
Information ChangeNegative|15612
Confidence Low|2876
Citation Hedged|895
-|-
Total|15796305

## Training procedure

This model was trained on a single 2.3 GHz Dual-Core Intel Core i5 with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805). 

## Eval results
### Overall
metric|test
-|-
f1 |.927
accuracy |.943

### By category
category|precision|recall|f1-score|support
-|-|-|-|-
AcademicTerms|0.91|0.92|0.92|486399
AcademicWritingMoves|0.76|0.82|0.79|20017
Character|0.94|0.95|0.94|1260272
Citation|0.92|0.94|0.93|50812
CitationAuthority|0.86|0.88|0.87|17798
CitationHedged|0.91|0.94|0.92|632
ConfidenceHedged|0.94|0.96|0.95|90393
ConfidenceHigh|0.92|0.94|0.93|113569
ConfidenceLow|0.79|0.81|0.80|2556
Contingent|0.92|0.94|0.93|81366
Description|0.87|0.89|0.88|1098598
Facilitate|0.87|0.90|0.89|41760
FirstPerson|0.96|0.98|0.97|330658
ForceStressed|0.93|0.94|0.93|436188
Future|0.90|0.93|0.92|93365
InformationChange|0.88|0.91|0.89|72813
InformationChangeNegative|0.83|0.85|0.84|12740
InformationChangePositive|0.82|0.86|0.84|22994
InformationExposition|0.94|0.95|0.95|468078
InformationPlace|0.95|0.96|0.96|147688
InformationReportVerbs|0.91|0.93|0.92|95563
InformationStates|0.95|0.95|0.95|139429
InformationTopics|0.90|0.92|0.91|328152
Inquiry|0.85|0.89|0.87|79030
Interactive|0.95|0.96|0.95|602857
MetadiscourseCohesive|0.97|0.98|0.98|195548
MetadiscourseInteractive|0.92|0.94|0.93|73159
Narrative|0.92|0.94|0.93|1023452
Negative|0.88|0.89|0.88|645810
Positive|0.87|0.89|0.88|409775
PublicTerms|0.91|0.92|0.91|184108
Reasoning|0.93|0.95|0.94|169208
Responsibility|0.83|0.87|0.85|21819
Strategic|0.88|0.90|0.89|193768
SyntacticComplexity|0.95|0.96|0.96|1635918
Uncertainty|0.87|0.91|0.89|33684
Updates|0.91|0.93|0.92|77760
-|-|-|-|-
micro avg|0.92|0.93|0.93|10757736
macro avg|0.90|0.92|0.91|10757736
weighted avg|0.92|0.93|0.93|10757736


## DocuScope Category Descriptions

Category (Cluster)|Description|Examples
-|-|-
Academic Terms|Abstract, rare, specialized, or disciplinary-specific terms that are indicative of informationally dense writing|*market price*, *storage capacity*, *regulatory*, *distribution*
Academic Writing Moves|Phrases and terms that indicate academic writing moves, which are common in research genres and are derived from the work of Swales (1981) and Cotos et al. (2015, 2017)|*in the first section*, *the problem is that*, *payment methodology*, *point of contention*
Character|References multiple dimensions of a character or human being as a social agent, both individual and collective|*Pauline*, *her*, *personnel*, *representatives*
Citation|Language that indicates the attribution of information to, or citation of, another source.|*according to*, *is proposing that*, *quotes from*
Citation Authorized|Referencing the citation of another source that is represented as true and not arguable|*confirm that*, *provide evidence*, *common sense*
Citation Hedged|Referencing the citation of another source that is presented as arguable|*suggest that*, *just one opinion*
Confidence Hedged|Referencing language that presents a claim as uncertain|*tends to get*, *maybe*, *it seems that*
Confidence High|Referencing language that presents a claim with certainty|*most likely*, *ensure that*, *know that*, *obviously*
Confidence Low|Referencing language that presents a claim as extremely unlikely|*unlikely*, *out of the question*, *impossible*
Contingent|Referencing contingency, typically contingency in the world, rather than contingency in one's knowledge|*subject to*, *if possible*, *just in case*, *hypothetically*
Description|Language that evokes sights, sounds, smells, touches and tastes, as well as scenes and objects|*stay quiet*, *gas-fired*, *solar panels*, *soft*, *on my desk*
Facilitate|Language that enables or directs one through specific tasks and actions|*let me*, *worth a try*, *I would suggest*
First Person|This cluster captures first person.|*I*, *as soon as I*, *we have been*
Force Stressed|Language that is forceful and stressed, often using emphatics, comparative forms, or superlative forms|*really good*, *the sooner the better*, *necessary*
Future|Referencing future actions, states, or desires|*will be*, *hope to*, *expected changes*
Information Change|Referencing changes of information, particularly changes that are more neutral|*changes*, *revised*, *growth*, *modification to*
Information Change Negative|Referencing negative change|*going downhill*, *slow erosion*, *get worse*
Information Change Positive|Referencing positive change|*improving*, *accrued interest*, *boost morale*
Information Exposition|Information in the form of expository devices, or language that describes or explains, frequently in regards to quantities and comparisons|*final amount*, *several*, *three*, *compare*, *80%*
Information Place|Language designating places|*the city*, *surrounding areas*, *Houston*, *home*
Information Report Verbs|Informational verbs and verb phrases of reporting|*report*, *posted*, *release*, *point out*
Information States|Referencing information states, or states of being|*is*, *are*, *existing*, *been*
Information Topics|Referencing topics, usually nominal subjects or objects, that indicate the “aboutness” of a text|*time*, *money*, *stock price*, *phone interview*
Inquiry|Referencing inquiry, or language that points to some kind of inquiry or investigation|*find out*, *let me know if you have any questions*, *wondering if*
Interactive|Addresses from the author to the reader or from persons in the text to other persons. The address comes in the language of everyday conversation, colloquy, exchange, questions, attention-getters, feedback, interactive genre markers, and the use of the second person.|*can you*, *thank you for*, *please see*, *sounds good to me*
Metadiscourse Cohesive|The use of words to build cohesive markers that help the reader navigate the text and signal linkages in the text, which are often additive or contrastive|*or*, *but*, *also*, *on the other hand*, *notwithstanding*, *that being said*
Metadiscourse Interactive|The use of words to build cohesive markers that interact with the reader|*I agree*, *let’s talk*, *by the way*
Narrative|Language that involves people, description, and events extending in time|*today*, *tomorrow*, *during the*, *this weekend*
Negative|Referencing dimensions of negativity, including negative acts, emotions, relations, and values|*does not*, *sorry for*, *problems*, *confusion*
Positive|Referencing dimensions of positivity, including actions, emotions, relations, and values|*thanks*, *approval*, *agreement*, *looks good*
Public Terms|Referencing public terms, concepts from public language, media, the language of authority, institutions, and responsibility|*discussion*, *amendment*, *corporation*, *authority*, *settlement*
Reasoning|Language that has a reasoning focus, supporting inferences about cause, consequence, generalization, concession, and linear inference either from premise to conclusion or conclusion to premise|*because*, *therefore*, *analysis*, *even if*, *as a result*, *indicating that*
Responsibility|Referencing the language of responsibility|*supposed to*, *requirements*, *obligations*
Strategic|This dimension is active when the text structures strategies activism, advantage-seeking, game-playing cognition, plans, and goal-seeking.|*plan*, *trying to*, *strategy*, *decision*, *coordinate*, *look at the*
Syntactic Complexity|The features in this category are often what are called “function words,” like determiners and prepositions.|*the*, *to*, *for*, *in*, *a lot of*
Uncertainty|References uncertainty, when confidence levels are unknown|*kind of*, *I have no idea*, *for some reason*
Updates|References updates that anticipate someone searching for information and receiving it|*already*, *a new*, *now that*, *here are some*



### BibTeX entry and citation info
```
@incollection{ishizaki2012computer,
  title    = {Computer-aided rhetorical analysis},
  author   = {Ishizaki, Suguru and Kaufer, David},
  booktitle= {Applied natural language processing: Identification, investigation and resolution},
  pages    = {276--296},
  year     = {2012},
  publisher= {IGI Global},
  url      = {https://www.igi-global.com/chapter/content/61054}
}
```
```
@article{DBLP:journals/corr/abs-1810-04805,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```