Does an algorithm exist to help detect the “primar

问题:

I'm trying to find out if there is a known algorithm that can detect the "key concept" of a sentence.

The use case is as follows:

1. User enters a sentence as a query (Does chicken taste like turkey?)
2. Our system identifies the concepts of the sentence (chicken, turkey)
3. And it runs a search of our corpus content

The area that we're lacking in is identifying what the core "topic" of the sentence is really about. The sentence "Does chicken taste like turkey" has a primary topic of "chicken", because the user is asking about the taste of chicken. While "turkey" is a helper topic of less importance.

So... I'm trying to find out if there is an algorithm that will help me identify the primary topic of a sentence... Let me know if you are aware of any!!!

回答1:

I actually did a research project on this and won two competitions and am competing in nationals.

There are two steps to the method:

1. Parse the sentence with a Context-Free Grammar
2. In the resulting parse trees, find all nouns which are only subordinate to Noun-Phrase-like constituents

For example, "I ate pie" has 2 nouns: "I" and "pie". Looking at the parse tree, "pie" is inside of a Verb Phrase, so it cannot be a subject. "I", however, is only inside of NP-like constituents. being the only subject candidate, it is the subject. Find an early copy of this program on http://www.candlemind.com. Note that the vocabulary is limited to basic singular words, and there are no verb conjugations, so it has "man" but not "men", has "eat" but not "ate." Also, the CFG I used was hand-made an limited. I will be updating this program shortly.

Anyway, there are limitations to this program. My mentor pointed out in its currents state, it cannot recognize sentences with subjects that are "real" NPs (what grammar actually calls NPs). For example, "that the moon is flat is not a debate any longer." The subject is actually "that the moon is flat." However, the program would recognize "moon" as the subject. I will be fixing this shortly.

Anyway, this is good enough for most sentences...

My research paper can be found there too. Go to page 11 of it to read the methods.

Hope this helps.

回答2:

Most of your basic NLP parsing techniques will be able to extract the basic aspects of the sentence - i.e., that chicken and turkey a NPs and they are linked by and adjective 'like', etc. Getting these to a 'topic' or 'concept' is more difficult

Technique such as Latent Semantic Analysis and its many derivatives transform this information into a vector (some have methods of retaining in some part the hierarchy/relations between parts of speech) and then compares them to existing, usually pre-classified by concept, vectors. See http://en.wikipedia.org/wiki/Latent_semantic_analysis to get started.

Edit Here's an example LSA app you can play around with to see if you might want to pursue it further . http://lsi.research.telcordia.com/lsi/demos.html

回答3:

For many longer sentences its difficult to say what exactly is a topic and also there may be more than one.

One way to get approximate ans is

1.) First tag the sentence using openNLP, stanford Parser or any one. 2.) Then remove all the stop words from the sentence. 3.) Pick up Nouns( proper, singular and plural).

Other way is

1.) chuck the sentence into phrases by any parser. 2.) Pick up all the noun phrases. 3.) Remove the Noun phrases that doesn't have the Nouns as a child. 4.) Keep only adjectives and Nouns, remove all words from remaining Noun Phrases.

This might give approx. guessing.

回答4:

"Key concept" is not a well-defined term in linguistics, but this may be a starting point: parse the sentence, find the subject in the parse tree or dependency structure that you get. (This doesn't always work; for example, the subject of "Is it raining?" is "it", while the key concept is likely "rain". Also, what's the key concept in "Are spaghetti and lasagna the same thing?")

This kind of problem (NLP + search) is more properly dealt with by methods such as LSA, but that's quite an advanced topic.

回答5:

On the most basic level, a question in English is usually in the form of <verb> <subject> ... ? or <pronoun> <verb> <subject> ... ?. This is by no means a good algorithm, especially considering that the subject could span several words, but depending on how sophisticated a solution you need, it might be a useful starting point.

If you need precision, ignore this answer.

回答6:

If you're willing to shell out money, http://www.connexor.com/ is supposed to be able to do this type of semantic analysis for a wide variety of languages, including English. I have never directly used their product, and so can't comment on how well it works.

回答7:

There's an article about Parsing Noun Phrases in the MIT Computational Linguistics journal of this month: http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00076

回答8:

Compound or complex sentences may have more than one key concept of a sentence.

You can use stanfordNLP or MaltParser which can give the dependency structure of a sentence. It also gives the parts of speech tagging including subject, verb , object etc.

I think most of the times the object will be the key concept of the sentence.

回答9:

You should look at Google's Cloud Natural Language API. It's their NLP service.

https://cloud.google.com/natural-language/

回答10:

Simple solution is to tag your sentence with part-of-speach tagger (e.g. from NLTK library for Python) then find matches with some predefined part-of-speach patterns in which it's clear where is main subject of the sentence

回答11:

One option is to look into something like this as a first step:

http://www.abisource.com/projects/link-grammar/

But how you derive the topic from these links is another problem in itself. But as Abiword is trying to detect grammatical problems, you might be able to use it to determine the topic.

回答12:

By "primary topic" you're referring to what is termed the subject of the sentence.

The subject can be identified by understanding a sentence through natural language processing.

The answer to this question is the same as that for How to determine subject, object and other words? - this is a currently unsolved problem.