URGENT Python programming task

Gather the knowledge necessary for Natural Logic inference (specialization/generalization from WordNet and LoreKB) or how to make a system that predicts the specialization/generalization of words, such as training a simple classifier from data. The format that you write it in can be anything that is easy to access and fully captures the information. In general, we would probably want them in EL or ULF universal implicatives (e.g. (all x [[x dog.n] -> [x animal.n]])). If we wanted something more complicated as we might see in the glosses of the words we could still do that in a uniform way (e.g. (all x [[x run.v] -> [x (quickly.adv-a run.v)]])). First, start with WordNet hypernym relations, you may opt to simply write it into a file that tab separates the relations: dog[tab]animal cat[tab]animal foot[tab]body_part .. It would be best to initially generate these files preserving the word sense in some way so we don't lose any information. The hypernym/hyponym relation is a very good place to start, but the information is incomplete so there are other options to expand. Lore knowledge base contains general world knowledge axioms that we could use. These are extracted from text, so they're relevant and not necessarily lexical (so a good pairing with WordNet info). The main issue with that source is that it's noisy, so we'll need to experiment with filtering/rule weighting to best use those axioms for inferences (you'll be able to run such experiments once I get the inference code working). There are other knowledge bases as well that may have interesting information (e.g. paraphrase database - PPDB, NELL knowledge base, Cyc; I included PPDB because synonyms are also relevant for inferences, so it would be useful to get that information). You can look over those datasets and see if there's any relevant data in them we can mine. For the classifier, the most straightforward thing would be to try to train a system to take a sentence, a word from the sentence, and a candidate word as inputs and outputs the hypernym relation between the word selection from the sentence and the candidate word, e.g.: Input : Coco is a dog, dog [span 3,4], animal Output: hyponym (between dog and animal) Using a token span rather than a token index (e.g. using [span 3,4] rather than [index 3]) will help us include compound nouns such as ‘university graduate’. So from 'Coco is a dog', Coco[0,1]; dog[3,4] and from 'Mary is a university graduate', university graduate[3,5]. You can generate a dataset by taking a sense disambiguated sentences ([login to view URL] and [login to view URL]). And then use the hypernym relations from wordnet to annotate the sentences. Then with that dataset you can train a system. An SVM classifier would be the simplest to get running with decent results (extract some features such as the sentence words, the words and characters surrounding the target word). [Notice that we're ignoring polarity, even though it's important. We're planning on handling polarity separately, so we don't have to worry about it here. We'll train the system to ignore polarity, but the sentence context is important to capture the word sense information]. please download lorekb file [login to view URL]