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Methods to extract keywords from large documents that are relevant to a set of predefined guidelines using NLP/ Semantic Similarity

I'm in need of suggestions how to extract keywords from a large document. The keywords should be inline what we have defined as the intended search results.
For example,
I need the owner's name, where the office is situated, what the operating industry is when a document about a company is given, and the defined set of words would be,
{owner, director, office, industry...}-(1)
the intended output has to be something like,
{Mr.Smith James, ,Main Street, Financial Banking}-(2)
I was looking for a method related to Semantic Similarity where sentences containing words similar to the given corpus (1), would be extracted, and using POS tagging to extract nouns from those sentences.
It would be a useful if further resources could be provided that support this approach.

What you want to do is referred to as Named Entity Recognition.
In Python there is a popular library called SpaCy that can be used for that. The standard models are able to detect 18 different entity types which is a fairly good amount.
Persons and company names should be extracted easily, while whole addresses and the industry might be more difficult. Maybe you would have to train your own model on these entity types. SpaCy also provides an API for training your own models.
Please note, that you need quite a lot of training data to have decent results. Start with 1000 examples per entity type and see if it's sufficient for your needs. POS can be used as a feature.
If your data is unstructured, this is probably one of most suited approaches. If you have more structured data, you could maybe take advantage of that.

Extracting personal attributes from text

I'd like to extract personal attributes from a text written by a person. e.g.,
I have always been interested in professional cycling. Being a single mother, it was never easy to find enough time to pursue a sport professionally. The best I could do was to go for short rides along Melbourne's beautiful beaches...
Ideally, I'd want to extract something like cycling: interest, female: gender, sports: interest, Melbourne: location. I think this is called named entity extraction, but I'm not sure. I tried Stanford Named Entity Recognizer and it didn't give me quite what I wanted. The most important things are personal attributes, such as gender, age, interests etc. and it missed most of these on different samples.
Is there any tool/library (preferably in Python) that can help me do this? I know about NLTK, but I don't know how/if I can utilize it here.

Normally the Stanford Named Entity Tagger have some default classifiers it's only have some general tagging like 'Name','Location','Organizations'. If you need to some other tagging you have to train your own classifier. You can refer this for create new classifier. I have created custom model and working fine.

Name Entity Resolution Algorithm

I was trying to build an entity resolution system, where my entities are,
(i) General named entities, that is organization, person, location,date, time, money, and percent.
(ii) Some other entities like, product, title of person like president,ceo, etc.
(iii) Corefererred entities like, pronoun, determiner phrase,synonym, string match, demonstrative noun phrase, alias, apposition.
From various literature and other references, I have defined its scope as I would not consider the ambiguity of each of the entity beyond its entity category. That is, I am taking Oxford of Oxford University
as different from Oxford as place, as the previous one is the first word of an organization entity and second one is the entity of location.
My task is to construct one resolution algorithm, where I would extract
and resolve the entities.
So, I am working out an entity extractor in the first place.
In the second place, if I try to relate the coreferences as I found from
various literatures like this seminal work, they are trying to work out
a decision tree based algorithm, with some features like, distance,
i-pronoun, j-pronoun, string match, definite noun
phrase, demonstrative noun phrase, number agreement feature,
semantic class agreement, gender agreement, both proper names, alias, apposition
etc.
The algorithm seems a nice one where enities are extracted with Hidden Markov Model(HMM).
I could work out one entity recognition system with HMM.
Now I am trying to work out a coreference as well as an entity
resolution system. I was trying to feel instead of using so many
features if I use an annotated corpus and train it directly with
HMM based tagger, with a view to solve a relationship extraction like,
*"Obama/PERS is/NA delivering/NA a/NA lecture/NA in/NA Washington/LOC, he/PPERS knew/NA it/NA was/NA going/NA to/NA be/NA
small/NA as/NA it/NA may/NA not/NA be/NA his/PoPERS speech/NA as/NA Mr. President/APPERS"
where, PERS-> PERSON
PPERS->PERSONAL PRONOUN TO PERSON
PoPERS-> POSSESSIVE PRONOUN TO PERSON
APPERS-> APPOSITIVE TO PERSON
LOC-> LOCATION
NA-> NOT AVAILABLE*
would I be wrong? I made an experiment with around 10,000 words. Early results seem
encouraging. With a support from one of my colleague I am trying to insert some
semantic information like,
PERSUSPOL, LOCCITUS, PoPERSM, etc. for PERSON OF US IN POLITICS, LOCATION CITY US, POSSESSIVE PERSON MALE, in the tagset to incorporate entity disambiguation at one go. My feeling relationship extraction would be much better now.
Please see this new thought too.
I got some good results with Naive Bayes classifier also where sentences
having predominately one set of keywords are marked as one class.
If any one may suggest any different approach, please feel free to suggest so.
I use Python2.x on MS-Windows and try to use libraries like NLTK, Scikit-learn, Gensim,
pandas, Numpy, Scipy etc.
Thanks in Advance.

It seems that you are going in three different paths that are totally different and each can be done in a stand alone Phd. There are many literature about them. My first advice focus on the main task and outsource the remaining. If you are going to develop this for non-famous language, also, you can build on others.
Named Entity Recognition
Standford NLP have really go too far in that specially for English. They resolve named entities really good, they are widely used and have a nice community.
Other solution may exist in openNLP for python .
Some tried to extend it to unusual fine-grain types but you need much bigger training data to cover the cases and the decision becomes much harder.
Edit: Stanford NER exists in NLTK python
Named Entity Resolution/Linking/Disambiguation
This is concerned with linking the name to some knowledge base, and solves the problem of whether Oxford University of Oxford City.
AIDA: is one of the state-of-art in that. They uses different context information as well as coherence information. Also, they have tried supporting several languages. They have a good bench mark.
Babelfy: offers interesting API that does NER and NED for Entities and concepts. Also, they support many language but never worked very well.
others like tagme and wikifi ...etc
Conference Resolution
Also Stanford CoreNLP has some good work in that direction. I can also recommend this work where they combined Conference Resolution with NED.

Named entity recognition : For new/latest entities

Sorry for that weird "question title" , but I couldnt think of an appropriate title.
Im new to NLP concepts, so I used NER demo (http://cogcomp.cs.illinois.edu/demo/ner/results.php). Now the issue is that "how & in what ways" can I use these taggings done by NER. I mean these what answers or inferences can one draw from these named-entities which have been tagged in certain groups - location, person ,organization etc. If I have a data which has names of entirely new companies, places etc then how am I going to do these NER taggings for such a data ?
Pls dont downvote or block me, I just need guidance/expert suggestions thats it. Reading about a concept is another thing, while being able to know where & when to apply it is another thing, which is where Im asking for guidance. Thanks a ton !!!
A snippet from the demo:-
Dogs have been used in cargo areas for some time, but have just been introduced recently in
passenger areas at LOC Newark and LOC JFK airports. LOC JFK has one dog and LOC Newark has a
handful, PER Farbstein said.

Usually NER is a step in a pipeline. For example, once all entities have been tagged, if you have many sentences like [PER John Smith], CEO of [ORG IBM] said..., then you can set up a table of Companies and CEOs. This is a form of knowledge base population.
There are plenty of other uses, though, depending on the type of data you already have and what you are trying to accomplish.

I think there are two parts in your question:
What is the purpose of NER?
This is vast question, generally it is used for Information Retrieval (IR) tasks such as indexing, document classification, Knowledge Base Population (KBP) but also many, many others (speech recognition, translation)... quite hard to figure out an extensive list...
How can we NER be extended to also recognize new/unkown entities?
E.g. how can we recognize entities that have never been seen by the NER system. In a glance, two solutions are likely to work:
Let's say you have some linked database that is updated on a regular basis: than the system may rely on generic categories. For instance, let's say "Marina Silva" comes up in news and is now added to lexicon associated to category "POLITICIAN". As the system knows that every POLITICIAN should be tagged as a person, i.e. doesn't rely on lexical items but on categories, and will thus tag "Marina Silva" as a PERS named entity. You don't have to re-train the whole system, just to update its lexicon.
Using morphological and contextual clues, the system may guess for new named entities that have never been seen (and are not in the lexicon). For instance, a rule like "The presidential candidate XXX YYY" (or "Marina YYY") will guess that "XXX YYY" (or just "YYY") is a PERS (or part of a PERS). This involves, most of the times, probabilistic modeling.
Hope this helps :)

How to extract meaning from sentences after running named entity recognition?

First: Any recs on how to modify the title?
I am using my own named entity recognition algorithm to parse data from plain text. Specifically, I am trying to extract lawyer practice areas. A common sentence structure that I see is:
1) Neil focuses his practice on employment, tax, and copyright litigation.
or
2) Neil focuses his practice on general corporate matters including securities, business organizations, contract preparation, and intellectual property protection.
My entity extraction is doing a good job of finding the key words, for example, my output from sentence one might look like this:
Neil focuses his practice on (employment), (tax), and (copyright litigation).
However, that doesn't really help me. What would be more helpful is if i got an output that looked more like this:
Neil focuses his practice on (employment - litigation), (tax - litigation), and (copyright litigation).
Is there a way to accomplish this goal using an existing python framework such as nltk (after my algo extracts the practice areas) can I use ntlk to extract the other words that my "practice areas" modify in order to get a more complete picture?

Named entity recognition (NER) systems typically use grammer-based rules or statistical language models. What you have described here seems to be based only on keywords, though.
Typically, and much like most complex NLP tasks, NER systems should be trained on domain-specific data so that they perform well on previously unseen (test) data. You will require adequate knowledge of machine learning to go down that path.
In "normal" language, if you want to extract words or phrases and categorize them into classes defined by you (e.g. litigation), if often makes sense to use category labels in external ontologies. An example could be:
You want to extract words and phrases related to sports.
Such a categorization (i.e. detecting whether or not a word is indeed related to sports) is not a "general"-enough problem. Which means you will not find ready-made systems that will solve the problem (e.g. algorithms in the NLTK library). You can, however, use an ontology like Wikipedia and exploit the category labels available there.
E.g., you can check that if you search Wikipedia for "football", which has a category label "ball games", which in turn is under "sports".
Note that the wikipedia category labels form a directed graph. If you build a system which exploits the category structure of such an ontology, you should be able to categorize terms in your texts as you see fit. Moreover, you can even control the granularity of the categorization (e.g. do you want just "sports", or "individual sports" and "team sports").
I have built such a system for categorizing terms related to computer science, and it worked remarkably well. The closest freely available system that works in a similar way is the Wikifier built by the cognitive computing group at the University of Illinois at Urbana-Champaign.
Caveat: You may need to tweak a simple category-based code to suit your needs. E.g. there is no wikipedia page for "litigation". Instead, it redirects you to a page titled "lawsuit". Such cases need to be handled separately.
Final Note: This solution is really not in the area of NLP, but my past experience suggests that for some domains, this kind of ontology-based approach works really well. Also, I have used the "sports" example in my answer because I know nothing about legal terminology. But I hope my example helps you understand the underlying process.

I do not think your "algo" is even doing entity recognition... however, stretching the problem you presented quite a bit, what you want to do looks like coreference resolution in coordinated structures containing ellipsis. Not easy at all: start by googling for some relevant literature in linguistics and computational linguistics. I use the standard terminology from the field below.
In practical terms, you could start by assigning the nearest antecedent (the most frequently used approach in English). Using your examples:
first extract all the "entities" in a sentence
from the entity list, identify antecedent candidates ("litigation", etc.). This is a very difficult task, involving many different problems... you might avoid it if you know in advance the "entities" that will be interesting for you.
finally, you assign (resolve) each anaphora/cataphora to the nearest antecedent.

Have a look at CogComp NER tagger:
https://github.com/CogComp/cogcomp-nlp/tree/master/ner