Code for the Spanish version of TAALES (TAALES Español)
This page includes the code for the Spanish Version of TAALES (TAALES Español) used in Diez-Ortega & Kyle (2023).
If you use TAALES_ES in your work, please cite the following paper:
Díez-Ortega, M., & Kyle, K. (2023). Measuring the development of lexical richness of L2 Spanish: A longitudinal learner corpus study. Studies in Second Language Acquisition, 1-31. https://doi.org/10.1017/S0272263123000384
In order to use this code, you will first need to have a working Python 3 environment. If this is your first time using Python, we suggest using the Anaconda distribution of Python.
You will also need to install (Spacy)[https://spacy.io/usage] and download/install the "es_core_news_sm" Spanish language model for Spacy. Spacy has excellent tutorials for installing both the Spacy package itself and its language models.
Finally, you will need to install the lexical-diversity package.
You can install this using pip:
pip install lexical-diversityOnce Spacy, the "es_core_news_sm" model, and lexical-diversity are installed, TAALES_ES can be run.
Make sure that your working directory includes the TAALES_ES_0501 script and the data files. Then, open a Python interpreter and run the following code:
import TAALES_ES_0501 as tes
tes.TAALES_ES("path_to_texts_files/","results.csv") #where path_to_texts_files/ is the name of the folder that your text files are inTAALES_ES will then process each text file in the target folder and produce a .csv file with the results for each text AND three files that include the index scores for each word/bigram in each file.
| Index Name | Index Category | Basic Description | Reference Corpus/Database | Details |
|---|---|---|---|---|
| filename | Basic Description | name of processed text file | n/a | n/a |
| nwords | Basic Description | number of word tokens | n/a | n/a |
| ncontentwords | Basic Description | number of content word tokens | n/a | n/a |
| MATTR_lemmas | Lexical Diversity | moving average type-token ratio (50-word window) | n/a | n/a |
| lemma_frequency_log_AW | Word Frequency | Average reference-corpus frequency (log-transformed) for all words | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| lemma_frequency_log_CW | Word Frequency | Average reference-corpus frequency (log-transformed) for content words | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| lemma_frequency_log_verbs | Word Frequency | Average reference-corpus frequency (log-transformed) for verbs | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| lemma_frequency_log_nouns | Word Frequency | Average reference-corpus frequency (log-transformed) for nouns | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| bigram_freq_log | Bigram Frequency | Average reference-corpus frequency (log-transformed) for bigrams | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| bigram_soa_dp_LR | Bigram Strength of Association | Average delta p score (left word as cue) for bigrams | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| bigram_soa_dp_RL | Bigram Strength of Association | Average delta p score (right word as cue) for bigrams | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| bigram_soa_dp_max | Bigram Strength of Association | Average max delta p score max(right word as cue, left word as cue) for bigrams | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| bigram_soa_MI | Bigram Strength of Association | Average mutual information score for bigrams | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| bigram_soa_T | Bigram Strength of Association | Average T score for bigrams | Spanish Corpus of the Web (ESCOW14; Schäfer, 2015; Schäfer & Bildhauer, 2012) | Section ax01 (450 million words) |
| valency_CW | Psycholinguistic Information | Average valency score for content words | Guash et al., 2016 | Includes ratings for 1,400 words |
| arousal_CW | Psycholinguistic Information | Average arousal score for content words | Guash et al., 2017 | Includes ratings for 1,400 words |
| concreteness_CW | Psycholinguistic Information | Average concreteness score for content words | Guash et al., 2018 | Includes ratings for 1,400 words |
| imageability_CW | Psycholinguistic Information | Average imageability score for content words | Guash et al., 2019 | Includes ratings for 1,400 words |
| availability_CW | Psycholinguistic Information | Average availability score for content words | Guash et al., 2020 | Includes ratings for 1,400 words |
| familiarity_CW | Psycholinguistic Information | Average familiarity score for content words | Guash et al., 2021 | Includes ratings for 1,400 words |
TAALES_ES is available for use under a Creative Commons Attribution-NonCommercial-Sharealike license (4.0)
For a summary of this license (and a link to the full license) click here.