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Results tagged with machine-learning
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user 46624
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
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Accepted
Classifying whether a comment or review is a complaint or appreciation of product and extrac...
Your (basic) task is sentiment analysis, covered in many places.
There is a number of algorithms proven good for that, including LSTM but you need a good deal of data to train that (and compute power) …
- 196
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Predict time of dispatch for marketing campaign
As there are important questions to the scenario and data, I'm sharing some thoughts together with assumed answers to some questions rather than a complete solution.
First of all, in sample data ther …
- 196
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Accepted
Algorithms that can determine whether a string is an English sentence?
To account for all your samples, first check if the text is English at all (solution as others hinted).
If yes, then there is a question what makes a 'complete English sentence'.
From your (two) sam …
- 196
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how much text data is required for a meaningful use of word2vec
You can download already generated vectors from
FastText https://fasttext.cc/docs/en/english-vectors.html for Wiki + some other web pages
and
SpaCy https://spacy.io/models/ - for Common Crawl
- 196
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What machine learning algorithms to use for unsupervised POS tagging?
Very interested to hear what you need a tagger for in the context of chatbots?
Maybe you need just a stemmer - to produce 'base form' for an inflected word?
In that case, you can check this.
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In natural language processing, why each feature requires an extra dimension?
Not sure how much about NLP you already digested, so shortly from the begining:
Text processing usually starts with tokenization into words (and other segments like numbers and punctuation marks) an …
- 196
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Why word2vec performs much worst than both CountVectorizer and TfidfVectorizer? [Text classi...
Check fastText pretrained vectors (https://fasttext.cc/docs/en/crawl-vectors.html) to have a starting point generated on a bigger corpus. Then you can take these vectors and train them further with yo …
- 196
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NER: Extracting entities from an article
CRF is a standard for such case but bi-LSTM + CRF are said to be even better (e.g. https://arxiv.org/pdf/1508.01991.pdf). Not sure if you need POS as this is usually solved using the same techniques - …
- 196
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Sequence models word2vec
Word2Vec operates on words and you want to compare 'texts' (series of words of varied length). For that, doc2vec might more appropriate.
You have very short 'texts' (names of campaigns) so generatin …
- 196