sense2vec: Use NLP to go beyond vanilla word2vec

sense2vec Trask et. al, 2015) is a nice
twist on word2vec that lets you
learn more interesting, detailed and context-sensitive word vectors. For an
interactive example of the technology, see our
sense2vec demo that lets you explore
semantic similarities across all Reddit comments of 2015.

This library is a simple Python/Cython implementation for loading and querying
sense2vec models. While it's best used in combination with
spaCy, the sense2vec library itself is very lightweight
and can also be used as a standalone module. See below for usage details.

? Version 1.0 alpha out now! Read the release notes here.

Usage Examples

Usage with spaCy

import spacy
from sense2vec import Sense2VecComponent

nlp = spacy.load("en_core_web_sm")
s2v = Sense2VecComponent("/path/to/reddit_vectors-1.1.0")
nlp.add_pipe(s2v)

doc = nlp("A sentence about natural language processing.")
assert doc[3].text == "natural language processing"
freq = doc[3]._.s2v_freq
vector = doc[3]._.s2v_vec
most_similar = doc[3]._.s2v_most_similar(3)
# [(('natural language processing', 'NOUN'), 1.0),
#  (('machine learning', 'NOUN'), 0.8986966609954834),
#  (('computer vision', 'NOUN'), 0.8636297583580017)]

Standalone usage without spaCy

import sense2vec

s2v = sense2vec.load("/path/to/reddit_vectors-1.1.0")
query = "natural_language_processing, NOUN"
assert query in s2v
freq, vector = s2v[query]
words, scores = s2v.most_similar(vector, 3)
most_similar = list(zip(words, scores))
# [('natural_language_processing, NOUN', 1.0),
#  ('machine_learning, NOUN', 0.8986966609954834),
#  ('computer_vision, NOUN', 0.8636297583580017)]

Installation & Setup

sense2vec releases are available on pip:

pip install sense2vec==1.0.0a0

The Reddit vectors model is attached to the
latest release. To load it
in, download the .tar.gz archive, unpack it and point sense2vec.load to
the extracted data directory:

import sense2vec
s2v = sense2vec.load("/path/to/reddit_vectors-1.1.0")

Usage

Usage with spaCy v2.x

The easiest way to use the library and vectors is to plug it into your spaCy
pipeline. Note that sense2vec doesn't depend on spaCy, so you'll have to
install it separately and download the English model.

pip install -U spacy==2.0.0
python -m spacy download en_core_web_sm

The sense2vec package exposes a Sense2VecComponent, which can be
initialised with the data path and added to your spaCy pipeline as a
custom pipeline component.
By default, components are added to the end of the pipeline, which is the
recommended position for this component, since it needs access to the dependency
parse and, if available, named entities.

import spacy
from sense2vec import Sense2VecComponent

nlp = spacy.load("en_core_web_sm")
s2v = Sense2VecComponent("/path/to/reddit_vectors-1.1.0")
nlp.add_pipe(s2v)

The pipeline component will merge noun phrases and entities according to
the same schema used when training the sense2vec models (e.g. noun chunks
without determiners like "the"). This ensures that you'll be able to retrieve
meaningful vectors for phrases in your text. The component will also add
serveral extension attributes and methods
to spaCy's Token and Span objects that let you retrieve vectors and
frequencies, as well as most similar terms.

doc = nlp("A sentence about natural language processing.")
assert doc[3].text == "natural language processing"
assert doc[3]._.in_s2v
freq = doc[3]._.s2v_freq
vector = doc[3]._.s2v_vec
most_similar = doc[3]._.s2v_most_similar(10)

For entities, the entity labels are used as the "sense" (instead of the
token's part-of-speech tag):

doc = nlp("A sentence about Facebook and Google.")
for ent in doc.ents:
    assert ent._.in_s2v
    most_similar = ent._.s2v_most_similar(3)

Available attributes

The following attributes are available via the ._ property – for example
token._.in_s2v:, Name, Attribute Type, Type, Description, ------------------, --------------, ------------------, ----------------------------------------------------------------------------------, in_s2v, property, bool, Whether a key exists in the vector map., s2v_freq, property, int, The frequency of the given key., s2v_vec, property, ndarray[float32], The vector of the given key., s2v_most_similar, method, list, Get the n most similar terms. Returns a list of ((word, sense), score) tuples., > ⚠️ A note on span attributes: Under the hood, entities in doc.ents are

Span objects. This is why the pipeline component also adds attributes and
methods to spans and not just tokens. However, it's not recommended to use the
sense2vec attributes on arbitrary slices of the document, since the model likely
won't have a key for the respective text. Span objects also don't have a
part-of-speech tag, so if no entity label is present, the "sense" defaults to
the root's part-of-speech tag.

Standalone usage

To use only the sense2vec library, you can import the package and then call
its load() method to load in the vectors.

import sense2vec
s2v = sense2vec.load("/path/to/reddit_vectors-1.1.0")

sense2vec.load returns an instance of the VectorMap class, which you
can interact with via the following methods.

⚠️ Important note: When interacting with the VectorMap directly, the
keys need to follow the scheme of phrase_text, SENSE (note the _ instead
of spaces and the , before the tag or label) – for example,
machine_learning, NOUN. Also note that the underlying vector table is
case-sensitive.

method VectorMap.__len__

The total number of entries in the map., Argument, Type, Description, -----------, ----, ---------------------------------, RETURNS, int, The number of entries in the map., ```python
s2v = sense2vec.load("/path/to/reddit_vectors-1.1.0")
assert len(s2v) == 1195261

#### <kbd>method</kbd> `VectorMap.__contains__`

Check whether the `VectorMap` has a given key. Keys consist of the word
string, a pipe and the "sense", i.e. the part-of-speech tag or entity label.
For example: `'duck, NOUN'` or `'duck, VERB'`. See the section on "Senses"
below for more details. Also note that the underlying vector table is
**case-sensitive**., Argument, Type, Description, -----------, -------, -----------------------------------, `string`, unicode, The key to check., **RETURNS**, bool, Whether the key is part of the map., ```python
assert "duck, NOUN" in s2v
assert "duck, VERB" in s2v
assert "dkdksl, VERB" not in s2v

method VectorMap.__getitem__

Retrieve a (frequency, vector) tuple from the vector map. The frequency is
an integer, the vector a numpy.ndarray(dtype='float32'). If the key is not
found, a KeyError is raised., Argument, Type, Description, -----------, -------, -------------------------------------------------, string, unicode, The key to retrieve the frequency and vector for., RETURNS, tuple, The (frequency, vector) tuple., ```python
freq, vector = s2v["duck, NOUN"]

#### <kbd>method</kbd> `VectorMap.__setitem__`

Assign a `(frequency, vector)` tuple to the vector map. The frequency should
be an integer, the vector a `numpy.ndarray(dtype='float32')`., Argument, Type, Description, --------, -------, ----------------------------------------------, `key`, unicode, The key to assign the frequency and vector to., `value`, tuple, The `(frequency, vector)` tuple to assign., ```python
freq, vector = s2v["avocado, NOUN"]
s2v["?, NOUN"] = (freq, vector)

method VectorMap.__iter__, VectorMap.keys

Iterate over the keys in the map, in order of insertion., Argument, Type, Description, ----------, -------, --------------------, YIELDS, unicode, The keys in the map., #### method VectorMap.values

Iterate over the values in the map, in order of insertion and yield
(frequency, vector) tuples from the vector map. The frequency is an integer,
the vector a numpy.ndarray(dtype='float32'), Argument, Type, Description, ----------, -----, ----------------------, YIELDS, tuple, The values in the map., #### method VectorMap.items

Iterate over the items in the map, in order of insertion and yield
(key, (frequency, vector)) tuples from the vector map. The frequency is an
integer, the vector a numpy.ndarray(dtype='float32'), Argument, Type, Description, ----------, -----, ---------------------, YIELDS, tuple, The items in the map., #### method VectorMap.most_similar

Find the keys of the n most similar entries, given a vector. Note that
the most similar entry with a score of 1.0 will be the key of the query
vector itself., Argument, Type, Description, -----------, --------------------------------, --------------------------------------------------, vector, numpy.ndarray(dtype='float32'), The vector to compare to., n, int, The number of entries to return. Defaults to 10., RETURNS, tuple, A (words, scores) tuple., ```python
freq, vector = s2v["avocado, NOUN"]
words, scores = s2v.most_similar(vector, n=3)
for word, score in zip(words, scores):
print(word, score)

avocado, NOUN 1.0

avacado, NOUN 0.970944344997406

spinach, NOUN 0.962776780128479

#### <kbd>method</kbd> `VectorMap.save`

Serialize the model to a directory. This will export three files to the output
directory: a `strings.json` containing the keys in insertion order, a
`freqs.json` containing the frequencies and a `vectors.bin` containing the
vectors., Argument, Type, Description, ----------, -------, ---------------------------------, `data_dir`, unicode, The path to the output directory., #### <kbd>method</kbd> `VectorMap.load`

Load a model from a directory. Expects three files in the directory (see
`VectorMap.save` for details)., Argument, Type, Description, ----------, -------, --------------------------------, `data_dir`, unicode, The path to load the model from., ## Senses

The pre-trained Reddit vectors support the following "senses", either
part-of-speech tags or entity labels. For more details, see spaCy's
[annotation scheme overview](https://spacy.io/api/annotation)., Tag, Description, Examples, -------, -------------------------, ------------------------------------, `ADJ`, adjective, big, old, green, `ADP`, adposition, in, to, during, `ADV`, adverb, very, tomorrow, down, where, `AUX`, auxiliary , is, has (done), will (do), `CONJ`, conjunction, and, or, but, `DET`, determiner, a, an, the, `INTJ`, interjection, psst, ouch, bravo, hello, `NOUN`, noun, girl, cat, tree, air, beauty, `NUM`, numeral, 1, 2017, one, seventy-seven, MMXIV, `PART`, particle, 's, not, `PRON`, pronoun, I, you, he, she, myself, somebody, `PROPN`, proper noun, Mary, John, London, NATO, HBO, `PUNCT`, punctuation, , ? ( ), `SCONJ`, subordinating conjunction, if, while, that, `SYM`, symbol, \$, %, =, :), ?, `VERB`, verb, run, runs, running, eat, ate, eating, Entity Label, Description, -------------, ----------------------------------------------------, `PERSON`, People, including fictional., `NORP`, Nationalities or religious or political groups., `FACILITY`, Buildings, airports, highways, bridges, etc., `ORG`, Companies, agencies, institutions, etc., `GPE`, Countries, cities, states., `LOC`, Non-GPE locations, mountain ranges, bodies of water., `PRODUCT`, Objects, vehicles, foods, etc. (Not services.), `EVENT`, Named hurricanes, battles, wars, sports events, etc., `WORK_OF_ART`, Titles of books, songs, etc., `LANGUAGE`, Any named language., ## Training a sense2vec model

> **? Under construction:** We're currently updating the training scripts for
> spaCy v2.x.