# Word Embeddings¶

Word embedding is a mapping of a word to a d-dimensional vector space. This real valued vector representation captures semantic and syntactic features. Polyglot offers a simple interface to load several formats of word embeddings.

```
from polyglot.mapping import Embedding
```

## Formats¶

The Embedding class can read word embeddings from different sources:

- Gensim word2vec objects: (
`from_gensim`

method) - Word2vec binary/text models: (
`from_word2vec`

method) - GloVe models (
`from_glove`

method) - polyglot pickle files: (
`load`

method)

```
embeddings = Embedding.load("/home/rmyeid/polyglot_data/embeddings2/en/embeddings_pkl.tar.bz2")
```

## Nearest Neighbors¶

A common way to investigate the space capture by the embeddings is to query for the nearest neightbors of any word.

```
neighbors = embeddings.nearest_neighbors("green")
neighbors
```

```
[u'blue',
u'white',
u'red',
u'yellow',
u'black',
u'grey',
u'purple',
u'pink',
u'light',
u'gray']
```

to calculate the distance between a word and the nieghbors, we can call
the `distances`

method

```
embeddings.distances("green", neighbors)
```

```
array([ 1.34894466, 1.37864077, 1.39504588, 1.39524949, 1.43183875,
1.68007386, 1.75897062, 1.88401115, 1.89186132, 1.902614 ], dtype=float32)
```

The word embeddings are not unit vectors, actually the more frequent the word is the larger the norm of its own vector.

```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
```

```
norms = np.linalg.norm(embeddings.vectors, axis=1)
window = 300
smooth_line = np.convolve(norms, np.ones(window)/float(window), mode='valid')
plt.plot(smooth_line)
plt.xlabel("Word Rank"); _ = plt.ylabel("$L_2$ norm")
```

This could be problematic for some applications and training algorithms. We can normalize them by \(L_2\) norms to get unit vectors to reduce effects of word frequency, as the following

```
embeddings = embeddings.normalize_words()
```

```
neighbors = embeddings.nearest_neighbors("green")
for w,d in zip(neighbors, embeddings.distances("green", neighbors)):
print("{:<8}{:.4f}".format(w,d))
```

```
white 0.4261
blue 0.4451
black 0.4591
red 0.4786
yellow 0.4947
grey 0.6072
purple 0.6392
light 0.6483
pink 0.6574
colour 0.6824
```

## Vocabulary Expansion¶

```
from polyglot.mapping import CaseExpander, DigitExpander
```

Not all the words are available in the dictionary defined by the word embeddings. Sometimes it would be useful to map new words to similar ones that we have embeddings for.

### Case Expansion¶

For example, the word `GREEN`

is not available in the embeddings,

```
"GREEN" in embeddings
```

```
False
```

we would like to return the vector that represents the word `Green`

,
to do that we apply a case expansion:

```
embeddings.apply_expansion(CaseExpander)
```

```
"GREEN" in embeddings
```

```
True
```

```
embeddings.nearest_neighbors("GREEN")
```

```
[u'White',
u'Black',
u'Brown',
u'Blue',
u'Diamond',
u'Wood',
u'Young',
u'Hudson',
u'Cook',
u'Gold']
```

### Digit Expansion¶

We reduce the size of the vocabulary while training the embeddings by
grouping special classes of words. Once common case of such grouping is
digits. Every digit in the training corpus get replaced by the symbol
`#`

. For example, a number like `123.54`

becomes `###.##`

.
Therefore, querying the embedding for a new number like `434`

will
result in a failure

```
"434" in embeddings
```

```
False
```

To fix that, we apply another type of vocabulary expansion
`DigitExpander`

. It will map any number to a sequence of `#`

s.

```
embeddings.apply_expansion(DigitExpander)
```

```
"434" in embeddings
```

```
True
```

As expected, the neighbors of the new number `434`

will be other
numbers:

```
embeddings.nearest_neighbors("434")
```

```
[u'##',
u'#',
u'3',
u'#####',
u'#,###',
u'##,###',
u'##EN##',
u'####',
u'###EN###',
u'n']
```

## Demo¶

Demo is available here.

### Citation¶

This work is a direct implementation of the research being described in the Polyglot: Distributed Word Representations for Multilingual NLP paper. The author of this library strongly encourage you to cite the following paper if you are using this software.

```
@InProceedings{polyglot:2013:ACL-CoNLL,
author = {Al-Rfou, Rami and Perozzi, Bryan and Skiena, Steven},
title = {Polyglot: Distributed Word Representations for Multilingual NLP},
booktitle = {Proceedings of the Seventeenth Conference on Computational Natural Language Learning},
month = {August},
year = {2013},
address = {Sofia, Bulgaria},
publisher = {Association for Computational Linguistics},
pages = {183--192},
url = {http://www.aclweb.org/anthology/W13-3520}
}
```