DistinctKeywords
This is a utility function to extract semantically distinct keywords. This is an unsupervised method based on word2vec. Current implementation used a word2vec model trained in simplewiki. Hilbert curve act as a Locality-sensitive hashing.
Methodology
After creating word2vec, the words are mapped to a hilbert space and the results are stored in a key-value pair (every word has a hilbert hash). Now for a new document, the words and phrases are cleaned, hashed using the dictionary. One word from each different prefix is then selected using wordnet ranking from NLTK (rare words are prioritized). The implementation of grouping and look up is made fast using Trie and SortedDict
Installation Instructions
- conda create -n keyphrases python=3.8 --no-default-packages
- conda activate keyphrases
- pip install distinct-keywords
- python -m spacy download en_core_web_sm
- conda install --channel=conda-forge nb_conda_kernels jupyter
- jupyter notebook
Model Files for processing (English)
Download the following files and keep it in the current working directory.
Benchmarks
Currently this is tested against KPTimes test dataset (20000 articles). A recall score of 31% is achieved when compared to the manual keywords given in the dataset. Steps to arrive at the score:
- Used both algorithms. Keybert was ran with additional parameter top_n=16 as the length of dstinct_keywords at 75% level was around 15.
- Results of algorithms and original keywords were cleaned (lower case, space removal, character removal, but no lemmatization)
- Take intersection of original keywords and generated keyword word banks (individual words)
- For each prediction compare the length of intersecting words with length of total keyword words
Output is given below
Usage
from distinct_keywords.keywords import DistinctKeywords
doc = """
Supervised learning is the machine learning task of learning a function that
maps an input to an output based on example input-output pairs. It infers a
function from labeled training data consisting of a set of training examples.
In supervised learning, each example is a pair consisting of an input object
(typically a vector) and a desired output value (also called the supervisory signal).
A supervised learning algorithm analyzes the training data and produces an inferred function,
which can be used for mapping new examples. An optimal scenario will allow for the
algorithm to correctly determine the class labels for unseen instances. This requires
the learning algorithm to generalize from the training data to unseen situations in a
'reasonable' way (see inductive bias).
"""
distinct_keywords=DistinctKeywords()
distinct_keywords.get_keywords(doc)
Output
['machine learning', 'pairs', 'mapping', 'vector', 'typically', 'supervised', 'bias', 'supervisory', 'task', 'algorithm', 'unseen', 'training']
from distinct_keywords.keywords import DistinctKeywords
#can also use it to extract keywords for multiple strings at once
docs = ["""Supervised learning is the machine learning task of learning a function that
maps an input to an output based on example input-output pairs. It infers a
function from labeled training data consisting of a set of training examples.
In supervised learning, each example is a pair consisting of an input object
(typically a vector) and a desired output value (also called the supervisory signal).
A supervised learning algorithm analyzes the training data and produces an inferred function,
which can be used for mapping new examples. An optimal scenario will allow for the
algorithm to correctly determine the class labels for unseen instances. This requires
the learning algorithm to generalize from the training data to unseen situations in a
'reasonable' way (see inductive bias).""",
"""K-Nearest Neighbours is one of the most basic yet essential classification algorithms
in Machine Learning. It belongs to the supervised learning domain and finds intense application
in pattern recognition, data mining and intrusion detection. It is widely disposable in real-life
scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about
the distribution of data (as opposed to other algorithms such as GMM, which assume a Gaussian
distribution of the given data)."""]
distinct_keywords=DistinctKeywords()
distinct_keywords.get_multiple_doc_keywords(docs)
Output
[['supervisory', 'typically', 'algorithm', 'supervised', 'unseen', 'machine learning', 'the training data', 'the machine', 'vector', 'training', 'task', 'bias', 'a function', 'Supervised learning', 'pairs', 'mapping'], ['meaning', 'supervised', 'opposed', 'intrusion', 'learning', 'K Nearest Neighbours', 'parametric', 'the most basic yet essential classification algorithms', 'pattern recognition', 'detection', 'the supervised learning domain', 'intense application', 'underlying', 'Machine Learning']]
German Model (On test)
I have added a german model based on word2vec found at https://devmount.github.io/GermanWordEmbeddings/ You can find it in the example folder