I'm trying to train my NLTK model to recognize movie names (ex. "game of thrones")
I have a text file where each line is a movie name.
How do I train my NLTK model to recognize these movie names if it sees it in a sentence during tokenization?
I searched around but found no resources. Any help is appreciated
It sounds like you are talking about training a named entity recognition (NER) model for movie names. To train an NER model in the traditional way, you'll need more than a list of movie names - you'll need a tagged corpus that might look something like the following (based on the 'dataset format' here):
I PRP O
like VBP O
the DT O
movie NN O
Game NN B-MOV
of IN I-MOV
Thrones NN I-MOV
. Punc O
but going on for a very long time (say, minimum 10,000 words to give enough examples of movie names in running text). Each word is following by the part-of-speech (POS) tag, and then the NER tag. B-MOV indicates that 'Game' is the start of a movie name, and I-MOV indicates that 'of' and 'Thrones' are 'inside' a movie name. (By the way, isn't Game of Thrones a TV series as opposed to a movie? I'm just reusing your example anyway...)
How would you create this dataset? Annotating by hand. It is a laborious process, but this is how state-of-the-art NER systems are trained, because whether or not something should be detected as a movie name depending on the context in which it appears. For example, there is a Disney movie called 'Aliens', but the same word 'Aliens' is a movie title in the second sentence below but not the first.
Aliens are hypothetical beings from other planets.
I went to see Aliens last week.
Tools like docanno exist to aid the annotation process. The dataset to be annotated should be selected depending on the final use case. For example, if you want to be able to find movie names in news articles, use a corpus of news articles. If you want to be able to find movie names in emails, use emails. If you want to be able to find movie names in any type of text, use a corpus with a wide range of different types of texts.
This is a good place to get started if you decide to stick with training and NER model using NLTK, although some of the answers here suggest other libraries you might want to use, such as spaCy.
Alternatively, if the whole tagging process sounds like too much work and you just want to use your list of movie names, look at fuzzy string matching. In this case, I don't think NLTK is the library to use as I'm not aware of any fuzzy string matching features in NLTK. You might instead use fuzzysearch as per the answer here.
Related
I am creating a medical web app that takes in audio input, converts it to text, and extracts keywords from the said text file, which is then used in an ML model. We have the text, but the problem lies in the fact that the person might say, I have pain in my chest and legs but the symptoms in our model are chest_pain or leg_pain.
How do we convert the different phrasing used by the user to one that matches our model features? Our basic approach would be using tokenizer and then using NLTK to check synonyms of each word and map pairs to try out multiple phrasings to match the one we currently have, but it would take one too much time.
Is it possible to do this task using basic NLP?
maybe an improvment of your first idea :
Split your keywords (chest_pain → ["chest","pain"]
Find only synonyms of your keywords ([["chest","thorax",...],["pain","suffer",...]]
For each words of your sentence check if the word is present in your keywords synonyms.
I want to implement a document text processing OCR in my flutter app. Most of the OCRs that I have seen can only read texts from images but cannot organize just the important information that is actually needed, E.G: Name, Lastname, Date of birth, Genre, etc. They can only read the whole thing. I discovered this page called "Nanonets" which does exactly what I need. You train the AI with images indicating only the data that you want and it works really well. The problem is that I cannot afford the pro plan, so I was wondering if there is an alternative way to create something similar by my own with maybe Tensorflow or another tool.
Here's the page if you wanna take a look to see what I mean: https://nanonets.com/
in my opine, you can't handle OCR text in organised manner without AI trained models. most of the AI model api service paid until and unless you trained your own AI models for that.
another way is you can try to clean your OCR Text data using apply NLP Natural language processing (NLP).However, it's not accurate as much as an AI trained model.
Apply regex and find email, contacts or pattern based data which we can easily identify by regex & eliminate from your actual string and apply NLP steps your self to get quick output.
few NLP terms/Rules and how its work:
Sentence Tokenization - dividing a string of written language into its component sentences. (string will split via punctuation mark)//. sentence boundary.
Word Tokenization - dividing a string of written language into its component words. (sentence will divide in to words to clean string).
Stop words - Stop words are words which are filtered out before or after processing of text to get accurate output. //remove irrelevant words like and, the, a
then apply other NLP terms like ...Text Lemmatization and Stemming,again regex to clean text again & bag of words, TF-IDF etc.
paid AI models & service for accurate result checkout this link which you can use. they provide AI services like scanning visiting card, scan docs etc.
Is there any efficient way to extract sub topic explanations of a review using python and NLTK library.As an example an user review regarding mobile phone could be "This phone's battery is good but display is a bullshit"
I wanna extract above two features like
"Battery is good"
"display is a bullshit"
The purpose of above is em gonna develop a rating system for products with respect to features of the product.
Analyzing polarity part has done.
But extracting features of review is some difficult for me.But I found a way to extract features using POS tag patterns with regular expressions like
<NN.?><VB.?>?<JJ.?>
this pattern as sub topic.But the problem is there could be lots of patterns in a review according to users description patterns.
Is there any way to solve my problem efficiently???
Thank you !!
The question you posed is multi-faceted and not straightforward to answer.
Conceptually, you may want to go through the following steps:
Identify the names of the features of phones (+ maybe creating an ontology based on these features).
Create a lists of synonyms to the feature names (similarly for evaluative phrases, e.g. nice, bad, sucks, etc.).
Use one of NLTK taggers to parse the reviews.
Create rules for extraction of features and their evaluation (Information Extraction part). I am not sure if NLTK can directly support you with this.
Evaluate and refine the approach.
Or: create a larger annotated corpus and train a Deep learning model on it using TensorFlow, Theano, or anything else alike.
I am new using Python and NLTK for NLP operations. Starting with different sentences I was wondering how I can extract certain dependent relations within a sentence.
For example:
Edward has a black jacket and white shoes with red laces
Using POS tagging I can extract certain parts of speech, but I want to specifically extract that he has, for example, a black jacket to ultimately list the information like:
Name: Edward
Clothing: Black jacket
Shoes: White shoes with red laces
What you're looking for is NER (Named Entity Recognition) . Since every sentence structure is different and information required from them are different you might need to make your own you get the template or working example from here.
There are also huge corpora available which you can use.
You can consider the problem as extracting relation tuples, may be as binary relations. In that case you need to know about open IE. In that case, you can extract relation tuples like, or . You can build your own relation extraction model if you have supervised data. Otherwise extracting name, clothing or other important information wouldn't be easy using other techniques like NER or POSTagging.
One alternative way can be dependency parsing but i am not sure how to model it for adapting to your particular needs.
Im working on a chat bot in python using nltk library. I want to use the POS tagger to classify my sentences into categories. For start I want to divide them into four categories "IMPERATIVE", "INTERROGATIVE", "EXCLAMATORY", "DECLARATIVE". Eventually I'd like to add categories like QUESTION, SALUTATION and APOLOGY. I'm looking for some reference on how english sentence patterns are defined. Something like a BNF for english sentences. Where can I find something like this.
Your task description doesn't sound like POS tagging but rather dialogue modelling: Essentially, you need to find a corpus of English sentences annotated according to their dialogue act type. One good annotation scheme I've worked with before is Allen and Core's Dialog Act Markup in Several Layers (DAMSL). You can also see their 1997 paper for more info on how this can be used, but unfortunately I don't know of any freely-available general-purpose corpora annotated with this data.