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| import { Grid, ThemeProvider, Typography } from "@mui/material"; | |
| import { buildTheme } from "../../infrastructure/theme/theme"; | |
| export default function Definition() { | |
| return ( | |
| <> | |
| <ThemeProvider theme={buildTheme()}> | |
| <Grid textAlign={"justify"} pl={2} pr={2}> | |
| <Typography variant="h3" color="#000" mb={1}> | |
| This is a simple web application that creates word embeddings using | |
| the gensim model. Word embeddings are numerical representations of | |
| words that capture their semantic meanings. Each of the 50 | |
| dimensions in a GloVe model represents a latent semantic attribute, | |
| and even tough they are not directly interpretable, with som sort of | |
| reverse engineering the embedding space the latent semantic | |
| attributes can be inferred. Two main approaches can be used to | |
| achieve this: | |
| </Typography> | |
| <Typography variant="h3" color="#000" mb={5}> | |
| <ol> | |
| <li> | |
| <Typography | |
| variant="h3" | |
| color="#000" | |
| sx={{ fontWeight: "600" }} | |
| > | |
| Investigating Common Category Attributes | |
| </Typography> | |
| <Typography variant="h3" color="#000" mb={2}> | |
| Investigating Common Category Attributes: By analyzing | |
| dimensions with the lowest variance among a group of | |
| semantically similar words.The premise is that dimensions with | |
| minimal variance may be capturing attributes that are common | |
| across the set. For instance: | |
| <ul> | |
| <li> | |
| <Typography variant="h3" color="#000"> | |
| <b>Words:</b> 'Spain', 'France', 'Germany', 'Japan' (all | |
| countries) | |
| </Typography> | |
| </li> | |
| <li> | |
| <Typography variant="h3" color="#000"> | |
| <b>Low Variance Dimensions:</b> These would | |
| theoretically indicate attributes common to all | |
| countries, potentially abstract notions like | |
| 'sovereignty', 'nationhood', or just the general | |
| category of being a 'country'. | |
| </Typography> | |
| </li> | |
| </ul> | |
| </Typography> | |
| </li> | |
| <li> | |
| <Typography | |
| variant="h3" | |
| color="#000" | |
| sx={{ fontWeight: "600" }} | |
| > | |
| Investigating Specific Semantic Differences: | |
| </Typography> | |
| <Typography variant="h3" color="#000"> | |
| By analyzing the high variance dimensions resulting from | |
| subtracting one vector from another. This subtraction aims to | |
| capture the core semantic differences between two entities. It | |
| can become more robust if more than one pair of words is | |
| selected to compare. For instance: | |
| </Typography> | |
| <ul> | |
| <li> | |
| <Typography variant="h3" color="#000"> | |
| <b>Words pairs:</b> 'Man' and 'Woman', 'Uncle' and 'Aunt', | |
| 'Father' and 'Mother'. | |
| </Typography> | |
| </li> | |
| <li> | |
| <Typography variant="h3" color="#000"> | |
| <b>Difference Vector (High Variance Dimensions)</b> : | |
| These dimensions likely highlight aspects related to | |
| gender differences. The highest values in this vector | |
| suggest dimensions where the concept of 'man' and 'woman' | |
| differ most significantly, potentially capturing | |
| gender-specific traits or roles. | |
| </Typography> | |
| </li> | |
| </ul> | |
| </li> | |
| </ol> | |
| </Typography> | |
| </Grid> | |
| </ThemeProvider> | |
| </> | |
| ); | |
| } | |