Paraphrase recognition using sentence level similarity

I'm a new entrant to NLP (Natural Language Processing). As a start up project, I'm developing a paraphrase recognizer (a system which can recognize two similar sentences). For that recognizer I'm going to apply various measures at three levels, namely: lexical, syntactic and semantic. At the lexical level, there are multiple similarity measure开发者_StackOverflows like cosine similarity, matching coefficient, Jaccard coefficient, et cetera. For these measures I'm using the simMetrics package developed by the University of Sheffield which contains a lot of similarity measures. But for the Levenshtein distance and Jaro-Winkler distance measures, the code is only at character level, whereas I require code at the sentence level (i.e. considering a single word as a unit instead of character-wise). Additionally, there is no code for computing the Manhattan distance in SimMetrics. Are there any suggestions for how I could develop the required code (or someone provide me the code) at the sentence level for the above mentioned measures?

Thanks a lot in advance for your time and effort helping me.

I have been working in the area of NLP for a few years now, and I completely agree with those who have provided answers/comments. This really is a hard nut to crack! But, let me still provide a few pointers:

(1) Lexical similarity: Instead of trying to generalize Jaro-Winkler distance to sentence-level, it is probably much more fruitful if you develop a character-level or word-level language model, and compute the log-likelihood. Let me explain further: train your language model based on a corpus. Then take a whole lot of candidate sentences that have been annotated as similar/dissimilar to the sentences in the corpus. Compute the log-likelihood for each of these test sentences, and establish a cut-off value to determine similarity.

(2) Syntactic similarity: So far, only stylometric similarities can manage to capture this. For this, you will need to use PCFG parse trees (or TAG parse trees. TAG = tree adjoining grammar, a generalization of CFGs).

(3) Semantic similarity: off the top of my head, I can only think of using resources such as Wordnet, and identifying the similarity between synsets. But this is not simple either. Your first problem will be to identify which words from the two (or more) sentences are "corresponding words", before you can proceed to check their semantics.

As Chris suggests, this is a non-trivial project for a beginner. I would suggest you start of something simpler (if relatively boring) such as chunking.

Have a look at the docs and books for the Python NLTK library - there are some samples that are close to what you are looking for. For example, containment: is it plausible that one statement contains another. note the 'plausible' there, the state of the art isn't good enough for a simple yes/no or even a probability.