Other options would be to...
Compare similar text sequences,
Compare similar string sequences,
Use fuzzy matching.
a <- data.frame(name = c('Ace Co', 'Bayes', 'asd', 'Bcy', 'Baes', 'Bays'),
price = c(10,...
Using NER (more generally sequence labeling) means classifying every token in the sentence, so if the goal is only to label every sentence there's no strong need for it in your case.
However NER might be more appropriate in case the order of the words is important, because sequence labeling models take it into account whereas traditional text classification ...
Word2Vec algorithms (Skip Gram and CBOW) treat each word equally,
because their goal to compute word embeddings. The distinction
becomes important when one needs to work with sentences or document
embeddings; not all words equally represent the meaning of a
particular sentence. And here different weighting strategies are
applied, TF-IDF is one of those ...
I'll suggest to test the sentence or the tweet for polarity. This can be done using the textblob library. It can be installed as pip install -U textblob. Once the text data polarity is found, it can be assigned as a separate column in the dataframe. Subsequently, the sentence polarity can then be used for further analysis.
Polarity and Subjectivity are ...
If I understand your question correctly, you have 2 sentences and you converted those sentences into 2 vectors, you want to know how those sentences are similar.
If this is the case use cosine similarity between 2 sentences and cosine similarity is a scalar not a vector its the dot product of your 2 embedding vectors.
from numpy import dot
from numpy.linalg ...
Paraphrase detection is still a very active and very challenging research area, so it's unlikely that there are full-fledged standard libraries for this task since there is still no clear "best solution" to this problem.
In order to build a corpus you might want to look at how shared tasks/competitions have done it before. I know at least of SemEval which ...
Chatbots and Q&A systems differ in their complexity as well as use cases. Let's discuss each of them separately.
They can answer various questions asked during an interactive conversation. Interactive conversion means the system keeps a track of questions asked earlier and can engage in longer conversations. They have a sought of memory which ...
Question-answering (QA) is sometimes used to refer to the task where the input to the system is a question and a list of possible answers (normally only a handful) or a paragraph where the answer is supposed to be found, and the expected answer is the index of the correct answer or the start/end positions where the answer located within the text.
In theory it's of course possible to reach perfect performance: if the algorithm can find what it needs in the features to correctly distinguish between classes (or clusters), then it will perform perfectly.
In reality however it's very rare that performance is perfect, because:
Text data is noisy and extremely diverse
Most of the time when there is a way ...
Well, obviously the use cases depends on the industry. Also, I am assuming you are thinking of use cases that are somehow useful. But let's think of some examples:
I once worked with a book distributor that tagged each book they sold with keywords (Fantasy, Horror, etc). You can automate the tagging process if you have a sufficiently large dataset of ...
What you need is simply a language model. This is a very common task so you should be able to find code and data easily. This question gives some pointers for Python (be careful, the accepted answer is incorrect according to the two other answers).
Applying the language model to a sentence gives you a probability (or a perplexity score, which works the ...
Similarly to NB or kNN, the DT and SVM algorithms work with the features which are provided as input. So whenever ML is applied to text it's important to understand how the unstructured text is transformed into structured data, i.e. how text instances are represented with features.
There are many options, but traditionally a document is represented as as a ...
What you're describing is indeed the traditional approach for building a sentiment analysis system, so I'd say it looks like a reasonable approach to me.
I'm not up to date with the sentiment analysis task at all, but I think it would be worth studying the state of the art for several reasons:
There might be more recent, better approaches
There might be ...
First check out the binary classification example in the scikit-learn documentation. It's as easy as that:
from sklearn.metrics import roc_curve
from sklearn.metrics import RocCurveDisplay
y_score = clf.decision_function(X_test)
fpr, tpr, _ = roc_curve(y_test, y_score, pos_label=clf.classes_)
roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()
In the ...
Here are a few ideas:
If the number of strings is not too high, you could consider taking a formal approach and use a finite automata determinization algorithm (I'm very rusty about this stuff but I clearly remember that there is such a thing). The idea is to start from a big automaton made of the union of all the strings, then use the algorithm to find the ...
The problem you face is part of what is called in literature grammar learning or grammar inference which is part of both Natural Language Processing and Machine Learning and in general is a very difficult problem.
However for certain cases like regular grammars/languages (ie learning regular expressions / DFA learning) there are satisfactory solutions up to ...
First about the features i think you could add some such as :
the time when the letter is received,
number of links in the email,
the whole structure (does it follow typical structure for email),
number of words that contains numbers in it,
what is the whole mood of the email (sales,threats,info,...-for this purpose you can use sentiment analysis),
I would like to make the argument that you actually cannot have statistically speaking 100.00% accuracy even in theory but you can get really close. However, you getting too close might mean that your overfitting. This is because you cannot have statistically speaking absolute zero uncertainty in any system of more than 2 predictors that are independent or ...
As mentioned by @Erwan you have to build named entity recognition model which will do your task easily. For understanding implementation of ner task you can refer to my notebook on kaggle which is based on similar dataset of flight rather cab. So it will help to custom build the dataset & upto certain extend use the prediction of my model.
It looks like you try everything but didn't design the system so that it does what you need it to do. In this task I don't see any reason to use things like LDA for instance. In my opinion this is a typical case for training a custom NE system which extracts specifically the targets you want. The first step is to annotate a subset of your data, for example ...
I think your best bet is a gazetteer approach. If you go to this link, you will find plenty of datasets containing lists of existing first names. This should help you detect a big majority of first names using regular expressions.
Now, there are some preprocessing steps you could take:
remove any digits, punctuation, etc.
if you have upper cases, you could ...
The compound split is not trivial, but there are solutions that kind of work (as you can read on the link @Aditya shared).
Another way of dealing with the matter would be to tokenize the strings into character n-grams.
This is a quite common mistake, you transformed the test and training data separately which messes with factor levels.
There are multiple solutions to this:
1) Create a common transformer
If you there is no "new" data but simply a test set (e.g. like in kaggle competitions or similar problems) you should create a function that transforms / tidies ALL data ...
To add onto @Nicholas James Bailey's answer:
tidytext provides functionality for two different main operations: text mining and text modeling.
I think the text mining part of it where we tokenize, tidy and prep text data is a bit more unique. As pointed out there are several model alternatives for text data, some of which are arguably better.
In terms of ...
Scikit-learn has a great implementation of latent dirichlet allocation, which I would argue is as straightforward to use as the implementation in tidytext. There’s a tutorial here.
Also, Python has SpaCy, which is slicker than anything R has so far in terms of tooling for NLP pipelines,
I do love R, and I feel it’s still a better language for tidying and ...
In case you have a lot of cases where the same brand is described in different ways, e.g. "K.F.C", "KFC", "Kentucky Fried Chicken", then it might be worth using clusters based on textual similarity measures. This kind of problem is similar to record linkage.
But if this is not the case, then it would be a bad idea to try to merge brands based on their names ...
This is an answer with tire tree:
#readindg stopword data
stopwords = pd.read_csv('STOPWORDS',header=None)
#creating tire tree
# Trie node class
self.children = [None]*15000
# isEndOfWord is True if node represent the end of the word
self.isEndOfWord = ...
I don't know much your data varies but I find that regular expressions can usually solve these sort of problems. In the example you gave you could, for instance, write these Python regular expressions that will extract the contract length and agreement fee.
# Contract Length
re.findall('\d+ (?:Weeks|Months|Years)', 'Mr xyz, this contact is valid for 3 ...