I have a parallel corpus for english-german. Is there a way to extract word alignment table from this corpus using nltk? I don't know if nltk.align is supposed to do this. I am unable to figure out from the documentation.
Look at the source of the modules in the nltk.translate package (previously known as nltk.align); you'll find descriptions of the available algorithms and references to the research literature that explains them in more detail.
Related
I'm trying to find the best way to compare two text documents using AI and machine learning methods. I've used the TF-IDF-Cosine Similarity and other similarity measures, but this compares the documents at a word (or n-gram) level.
I'm looking for a method that allows me to compare the meaning of the documents. What is the best way to do that?
You should start reading about word2vec model.
use gensim, get the pretrained model of google.
For vectoring a document, use Doc2vec() function.
After getting vectors for all your document, use some distance metric like cosine distance or euclidean distance for comparison.
This is very difficult. There is actually no computational definition of "meaning". You should dive into text mining, summarization and libraries like gensim, spacy or pattern.
In my opinion, the more readily useable libraries available out there ie. higher return on investesment (ROI), that is if you are a newbie you might want to look at tools around chatbots they want to extract from natural language structured data. That is what is the most similar to "meaning". One example free software tool to achieve that is rasa natural language understanding.
The drawback of such tools is that they somewhat work but only in the domain where they were trained and prepared to work. And in particular they do not aim at comparing documents like you want.
I'm trying to find the best way to compare two text documents using AI
You must come up with a more precise task and from there find out which technic apply best to your use case. Do you want to classify documents in predefined categories. Do you to compute some similarity between two documents? Given an input document, do you want to find most similar documents in a database. Do you want to extract important topics or keywords in the document? Do you want to summarize the document? Is it an abstractig summary or key phrase extraction?
In particular, there is no software that allows to extract somekind of semantic fingerprint from any document. Depending on the end goal, the way to achieve it might be completly different.
You must narrow the precise goal you are trying to achieve; From there, you will be able to ask another question (or improve this one) to describe precisly your goal.
Text understanding is AI-Complete. So, just saying to the computer "tell me something about this two documents" doesn't work.
Like other have said, word2vec and other word embeddings are tools to achieve many goals in NLP but it only a mean for an end. You must define the input and output of the system you are trying to design to be able to start working on the implementation.
There is two other Stack Overflow communities that you might want to dig:
Linguistics
Data Science
Given the tfidf value for each token in your corpus (or the most meaningful ones) you can compute a sparse representation for a document.
This is implemented in the sklearn TFIDFVectorizer.
As other users have pointed out, this is not the best solution to your task.
You should take into account embeddings.
The easiest solution consists in using an embedding at the words level, such as the one provided by the FastText framework.
Then you can create an embedding for the whole document by summing together the embedding of the single words which compose it.
An alterative consists in training an embedding directly at the document level, using some Doc2Vec framework such as the gensim or DL4J one.
Also you can use LDA Or LSI Models for text corpus. these methods(and other methods like wor2vec and doc2vec) can summarize documents to fixed length vectors with respect to it's meaning and topics that this document belongs to.
read more:
https://radimrehurek.com/gensim/models/ldamodel.html
I heard there are three approaches from Dr. Golden:
- Cosine Angular Separation
- Hamming Distance
- Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI)
These methods are based on semantic similarity.
I also heard some company used tool called Spacy to summarize document to compare each other.
I am looking for an approach in NLP , where i can generate a concept tree from a set of keywords.
Here is the scenario, i have extracted a set of keywords from a research paper. Now i want to arrange these keywords in form of a tree where most general keyword comes on top. At next level of tree will have keywords that are important to understand upper level concept and will be more specific as compared to upper level keywords. And the same way tree will grow.
Something like this :
I know there are many resources that can help me to solve this problem. Like Wikipedia dataset, Wordnet. But i do not know how to proceed with them.
My preferred programming language is Python. Do you know any python library or package which generate this?
I am also very interested to see the use of Machine learning approach to solve this problem.
I will really appreciate your any kind of help.
One way of looking at the problem is, given a set of documents, identify topics from them and also the dependencies between the topics.
So, for example, if you have some research papers as input (large set of documents), the output would be what topics the papers are on and how those topics are related in a hierarchy/tree. One research area that tries to tackle this is Hierarchical topic modeling and you can read more about this here and here.
But if you are just looking at creating a tree out of a bunch of keywords (that are somehow obtained) and no other information is available, then it needs knowledge of real world relationships and can perhaps be a rule-based system where we define Math --> Algebra and so on.
There is no way for a system to understand that algebra comes under math other than by looking at large no. of documents and inferring that relationship (see first suggestion) or if we manually map that relationship (perhaps, a rule-based system). That is how even humans learn those relationships.
Dear all I am working on a project in which I have to categories research papers into their appropriate fields using titles of papers. For example if a phrase "computer network" occurs somewhere in then title then this paper should be tagged as related to the concept "computer network". I have 3 million titles of research papers. So I want to know how I should start. I have tried to use tf-idf but could not get actual results. Does someone know about a library to do this task easily? Kindly suggest one. I shall be thankful.
If you don't know categories in advance, than it's not classification, but instead clustering. Basically, you need to do following:
Select algorithm.
Select and extract features.
Apply algorithm to features.
Quite simple. You only need to choose combination of algorithm and features that fits your case best.
When talking about clustering, there are several popular choices. K-means is considered one of the best and has enormous number of implementations, even in libraries not specialized in ML. Another popular choice is Expectation-Maximization (EM) algorithm. Both of them, however, require initial guess about number of classes. If you can't predict number of classes even approximately, other algorithms - such as hierarchical clustering or DBSCAN - may work for you better (see discussion here).
As for features, words themselves normally work fine for clustering by topic. Just tokenize your text, normalize and vectorize words (see this if you don't know what it all means).
Some useful links:
Clustering text documents using k-means
NLTK clustering package
Statistical Machine Learning for Text Classification with scikit-learn and NLTK
Note: all links in this answer are about Python, since it has really powerful and convenient tools for this kind of tasks, but if you have another language of preference, you most probably will be able to find similar libraries for it too.
For Python, I would recommend NLTK (Natural Language Toolkit), as it has some great tools for converting your raw documents into features you can feed to a machine learning algorithm. For starting out, you can maybe try a simple word frequency model (bag of words) and later on move to more complex feature extraction methods (string kernels). You can start by using SVM's (Support Vector Machines) to classify the data using LibSVM (the best SVM package).
The fact, that you do not know the number of categories in advance, you could use a tool called OntoGen. The tool basically takes a set of texts, does some text mining, and tries to discover the clusters of documents. It is a semi-supervised tool, so you must guide the process a little, but it does wonders. The final product of the process is an ontology of topics.
I encourage you, to give it a try.
I have a bunch of text documents that describe diseases. Those documents are in most cases quite short and often only contain a single sentence. An example is given here:
Primary pulmonary hypertension is a progressive disease in which widespread occlusion of the smallest pulmonary arteries leads to increased pulmonary vascular resistance, and subsequently right ventricular failure.
What I need is a tool that finds all disease terms (e.g. "pulmonary hypertension" in this case) in the sentences and maps them to a controlled vocabulary like MeSH.
Thanks in advance for your answers!
Here are two pipelines that are specifically designed for medical document parsing:
Apache cTAKES
NLM's MetaMap
Both use UMLS, the unified medical language system, and thus require that you have a (free) license. Both are Java and more or less easy to set up.
See http://www.ebi.ac.uk/webservices/whatizit/info.jsf
Whatizit is a text processing system that allows you to do textmining
tasks on text. The tasks come defined by the pipelines in the drop
down list of the above window and the text can be pasted in the text
area.
You could also ask biostars: http://www.biostars.org/show/questions/
there are many tools to do that. some popular ones:
NLTK (python)
LingPipe (java)
Stanford NER (java)
OpenCalais (web service)
Illinois NER (java)
most of them come with some predefined models, i.e. they've already been trained on some general datasets (news articles, etc.). however, your texts are pretty specific, so you might want to first constitute a corpus and re-train one of those tools, in order to adjust it to your data.
more simply, as a first test, you can try a dictionary-based approach: design a list of entity names, and perform some exact or approximate matching. for instance, this operation is decribed in LingPipe's tutorial.
Open Targets has a module for this as part of LINK. It's not meant to be used directly so it might require some hacking and tinkering, but it's the most complete medical NER (named entity recognition) tool I've found for python. For more info, read their blog post.
a bash script that has as example a lexicon generated from the disease ontology:
https://github.com/lasigeBioTM/MER
I am trying to implement a naive bayseian approach to find the topic of a given document or stream of words. Is there are Naive Bayesian approach that i might be able to look up for this ?
Also, i am trying to improve my dictionary as i go along. Initially, i have a bunch of words that map to a topics (hard-coded). Depending on the occurrence of the words other than the ones that are already mapped. And depending on the occurrences of these words i want to add them to the mappings, hence improving and learning about new words that map to topic. And also changing the probabilities of words.
How should i go about doing this ? Is my approach the right one ?
Which programming language would be best suited for the implementation ?
Existing Implementations of Naive Bayes
You would probably be better off just using one of the existing packages that supports document classification using naive Bayes, e.g.:
Python - To do this using the Python based Natural Language Toolkit (NLTK), see the Document Classification section in the freely available NLTK book.
Ruby - If Ruby is more of your thing, you can use the Classifier gem. Here's sample code that detects whether Family Guy quotes are funny or not-funny.
Perl - Perl has the Algorithm::NaiveBayes module, complete with a sample usage snippet in the package synopsis.
C# - C# programmers can use nBayes. The project's home page has sample code for a simple spam/not-spam classifier.
Java - Java folks have Classifier4J. You can see a training and scoring code snippet here.
Bootstrapping Classification from Keywords
It sounds like you want to start with a set of keywords that are known to cue for certain topics and then use those keywords to bootstrap a classifier.
This is a reasonably clever idea. Take a look at the paper Text Classication by Bootstrapping with Keywords, EM and Shrinkage by McCallum and Nigam (1999). By following this approach, they were able to improve classification accuracy from the 45% they got by using hard-coded keywords alone to 66% using a bootstrapped Naive Bayes classifier. For their data, the latter is close to human levels of agreement, as people agreed with each other about document labels 72% of the time.