Performing semantic analysis in text

Question

I want to perform semantic analysis on some text similar to YAGO[1]. But I have no structure in the text to identify entities and relationships. One way is I use POS tagging and then identify subject and predicates in the sentences[2]. But still I cannot establish what relationships exist between them. How should I go about this?

For example:

Albert Einstein was born in 1879.

Should result in:

AlbertEinstein BORNIN   1879
subject        relation predicate

My aim to look for better approaches to find subjects, predicates and relationships in raw text.

Answer 1

What you are trying to do is essentially Natural Language Understanding, a subfield of Natural Language Processing, which again is a subfield of Computational Linguistics ~ often thought as the engineering arm.

You could do semantic parsing or relation extraction. Either are fine for this task. I decided to read through Suchanek et al (2007) and you will realise that it is ontology based, where the relations are extracted into a predefined ontological template where aixoms are predifed (e.g. BORNIN). I personally think this is far to restrictive for general intelligence but works great with weak ai problems [narrow domains]. Much more interesting work has been happening over the years such as ontology driven information extraction, where the algorithms are trained on the ontology rather than having a corpus annotated by an ontology. One PhD study that comes to mind is McDowell Thesis and the Yildiz & Miksch (2007) paper.

Regardless and without going off topic, there is a really interesting open source Python GUI driven project called iepy at the moment being developed by a firm called Machinalis which is based on django. It allows for rule based and machine learning based information extraction. I highly recommend you check it out -> Tried and tested by myself. Also, I'm not affiliated with this company.

https://github.com/machinalis/iepy

According to the documentation:

IEPY is an open source tool for Information Extraction focused on Relation Extraction.

To give an example of Relation Extraction, if we are trying to find a birth date in:

"John von Neumann (December 28, 1903 – February 8, 1957) was a Hungarian and American pure and applied mathematician, physicist, inventor and polymath." then IEPY's task is to identify "John von Neumann" and "December 28, 1903" as the subject and object entities of the "was born in" relation.

It's aimed at: users needing to perform Information Extraction on a large dataset. scientists wanting to experiment with new IE algorithms.

Answer 2

Stanford parser can do it :) You need to look at the dependency parser though. Have a look at the bottom of this page: http://nlp.stanford.edu/software/lex-parser.shtml:

subject: nsubj(snapped, rain), or direct object: dobj(shut, hub)) ...

Or have a look at this page (Stanford Dependencies): http://nlp.stanford.edu/software/stanford-dependencies.shtml

And to understand the annotations have a look at this: http://nlp.stanford.edu/software/dependencies_manual.pdf

And for your particular example, use Stanford "collapsed" dependency parser which for a given sentence will produce predicates like born_in(Einstein,1879), which is very similar to what you want.

Answer 3

The task you attempt to solve is called relation extraction, while semantic analysis has much broader meaning (honestly, I can't say for sure what does it mean now).

Relation extraction is an open research problem, so I suggest to review the field - for example, start from the chapter 2.3 of Mining text data book or A Review of Relation Extraction paper (which is a little older - 2007). Then continue research by following citing or cited-by links; finally, try to implement approach that looks most promising: for example, if you know that your data is rather formal (all sentences are short and share similar strict structure), then try something like pattern-based approaches; and so on.