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import streamlit as st | |
import time | |
import numpy as np | |
import pandas as pd | |
from filter_dataframe import filter_dataframe | |
def get_typology_df(): | |
return pd.read_csv("data/language_typology.tsv", sep="\t") | |
st.set_page_config(page_title="Language Typology", page_icon="📈") | |
st.markdown("# Language Typology") | |
st.write("""\ | |
Languages can be described using hundreds of linguistic features. [World Atlas of Language Structures](https://doi.org/10.5281/zenodo.7385533) lists almost 200 different features. Since our work focuses on sentiment classification, we select 10 features that seem to be the most relevant to the task of sentiment expression. | |
The table below presents the languages included in the MMS corpus, their family, genus and their values for the selected linguistic features. | |
You can use the **'Add filters'** checkbox to filter the table by any of the columns.""") | |
df = get_typology_df() | |
st.dataframe(filter_dataframe(df)) |