Extracting Narrative Patterns in Different Textual Genres: A Multilevel Feature Discourse Analysis
Abstract
:1. Introduction
- (RQ1) Which linguistic features seem to be generally linked to each textual genre, and how prevalent are they?
- (RQ2) Is it possible to establish a connection between particular linguistic features and certain genres, given their main communicative purposes?
2. Related Work
3. Data and Tools
3.1. Corpora Collection
3.2. Linguistic Processing
- Freeling [45], a popular multilingual tool that allows us to obtain lexical, syntactic, and semantic information from a document. For example, features such as the presence of types of phrases, specific grammatical elements, or named entities were obtained thanks to this tool.
- AllenNLP [46]. This tool was used for the particular task of coreference resolution, as AllenNLP currently represents most of the state of the art on this specific research topic. Indeed, Freeling also includes a coreference resolution module, but it was observed that AllenNLP gave more adequate and complete results for the purpose of the present study. The coreference resolution model used is a model based on [47].
- CAEVO (Cascading Event Ordering system) [48], a tool capable of extracting and classifying discursive information related to events, time, and temporal expressions. For this purpose, it takes into account the TimeML specification [49], according to which an event refers to something that occurs or happens, and can be articulated by different kinds of expressions such as verbs, nominalizations, or adjectives. In addition, the tool classifies events semantically into one of seven categories: aspectual, perception, state, reporting, intensional action, intensional state, and occurrence. With this tool it is possible to extract all the interesting information regarding the event phenomena, not only with the terms that the tool identifies as events, but also their semantic environment.
4. Multilevel Feature Study of the Genres
4.1. Shallow Features
4.1.1. Word Length
4.1.2. Commas
4.2. Part-of-Speech (POS) and Syntactic Features
4.2.1. Nouns and Proper Nouns
4.2.2. Personal Pronouns
4.2.3. Adjectives and Adverbs/Wh-Adverbs
4.2.4. Verbal Tenses
4.2.5. Nonfinite Verb Forms
4.2.6. Predicative Complement
4.2.7. Figures
4.3. Semantic Features
4.3.1. Events
4.3.2. Time Links
4.3.3. Named Entity Length
4.3.4. Parenthesis, Interrogative, and Exclamatory Sentences
4.3.5. Subject and Object Dependency Relations
4.4. Discourse-Related Features
4.4.1. Coreference
4.4.2. Discourse Markers
4.4.3. Time Expressions
4.4.4. Quotation Marks
5. Overall and Final Remarks
6. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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# Docs | # Sents | # Words | Sents/Doc | Words/Doc | Words/Sent | |
---|---|---|---|---|---|---|
News | 487 | 12,565 | 274,153 | 26 | 563 | 26 |
Reviews | 447 | 17,150 | 306,120 | 38 | 685 | 21 |
Tales | 496 | 16,236 | 344,606 | 33 | 695 | 24 |
Features | News | Reviews | Tales |
---|---|---|---|
Word length 1 | 77.37 | 123.79 | 129.11 |
Word length 2 | 97.55 | 111.09 | 117.98 |
Word length 3 | 105.07 | 145.62 | 181.90 |
Word length 4 | 81.50 | 124.25 | 142.56 |
Word length 5 | 55.36 | 77.01 | 78.78 |
Word length 6 | 46.85 | 51.27 | 52.52 |
Word length 7 | 160.72 | 130.85 | 95.94 |
Punctuation, commas | 32.90 | 33.61 | 58.99 |
Features | News | Reviews | Tales |
---|---|---|---|
Nouns | 168.57 | 164.23 | 139.68 |
Proper nouns | 46.81 | 33.68 | 22.84 |
1st-person pronoun | 3.63 | 17.02 | 10.73 |
2nd-person pronoun | 0.53 | 5.85 | 4.46 |
3rd-person pronoun | 11.98 | 9.94 | 34.08 |
Adjectives | 32.22 | 46.36 | 38.35 |
Adverbs | 23.84 | 48.81 | 52.24 |
When-adverb | 0.99 | 1.85 | 3.43 |
Wh-adverb | 1.90 | 3.52 | 6.07 |
Present tense | 20.94 | 47.85 | 17.27 |
Past tense | 29.05 | 18.26 | 61.42 |
Future verb form | 1.84 | 1.86 | 1.99 |
Infinitive verb form | 19.83 | 27.64 | 32.00 |
Gerund verb form | 13.03 | 14.71 | 12.02 |
Participle verb form | 18.73 | 14.64 | 17.91 |
Predicative complement | 18.02 | 34.20 | 28.92 |
Figures | 24.24 | 40.87 | 11.67 |
Features | News | Reviews | Tales |
---|---|---|---|
Events (general) | 68.58 | 66.62 | 97.62 |
Aspectual event | 0.69 | 0.46 | 0.82 |
Intensional action event | 4.18 | 2.85 | 6.41 |
Intensional state event | 3.35 | 6.49 | 3.89 |
Occurrence event | 49.77 | 52.29 | 77.51 |
Perception event | 0.22 | 0.64 | 0.80 |
Reporting event | 8.69 | 1.66 | 5.37 |
State event | 1.69 | 2.33 | 2.81 |
Time links general (Tlinks) | 381.44 | 240.80 | 592.92 |
Before Tlink | 46.55 | 29.82 | 104.08 |
After Tlink | 36.90 | 17.12 | 67.02 |
Includes Tlink | 1.79 | 0.99 | 1.74 |
Is included Tlink | 15.58 | 5.79 | 9.94 |
Simultaneous Tlink | 1.93 | 1.66 | 2.77 |
Vague Tlink | 278.68 | 185.43 | 407.38 |
Parenthesis sentences | 0.00 | 3.91 | 0.00 |
Interrogative sentences | 0.21 | 1.75 | 2.42 |
Exclamatory sentences | 0.06 | 2.21 | 2.85 |
Nominal subject | 54.09 | 76.02 | 91.69 |
Direct object | 31.37 | 38.44 | 42.86 |
Named entity length 1 (NE 1) | 31.12 | 22.21 | 19.79 |
NE length 2 | 9.97 | 8.46 | 2.34 |
NE length 3 | 3.46 | 2.00 | 0.43 |
NE length 4 | 1.30 | 0.66 | 0.12 |
NE length 5 or more | 0.96 | 0.42 | 0.16 |
Features | News | Reviews | Tales |
---|---|---|---|
Coreference chains | 16.47 | 15.25 | 18.33 |
Maximal coreference chains | 7.94 | 7.60 | 10.36 |
Discourse markers (DMs) | 13.05 | 20.74 | 22.61 |
Time expressions general (timex) | 12.06 | 6.23 | 7.39 |
Date timex | 6.83 | 2.66 | 3.07 |
Time timex | 0.74 | 0.66 | 1.29 |
Duration timex | 4.21 | 2.65 | 2.71 |
Set timex | 0.28 | 0.26 | 0.33 |
Quotation marks | 6.23 | 0.10 | 12.32 |
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Maestre, M.M.; Vicente, M.; Lloret, E.; Cueto, A.S. Extracting Narrative Patterns in Different Textual Genres: A Multilevel Feature Discourse Analysis. Information 2023, 14, 28. https://doi.org/10.3390/info14010028
Maestre MM, Vicente M, Lloret E, Cueto AS. Extracting Narrative Patterns in Different Textual Genres: A Multilevel Feature Discourse Analysis. Information. 2023; 14(1):28. https://doi.org/10.3390/info14010028
Chicago/Turabian StyleMaestre, María Miró, Marta Vicente, Elena Lloret, and Armando Suárez Cueto. 2023. "Extracting Narrative Patterns in Different Textual Genres: A Multilevel Feature Discourse Analysis" Information 14, no. 1: 28. https://doi.org/10.3390/info14010028