Magnitude google
Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications. We present Magnitude, a fast, lightweight tool for utilizing and processing embeddings. Magnitude is an open source Python package with a compact vector storage file format that allows for efficient manipulation of huge numbers of embeddings. Magnitude performs common operations up to 60 to 6,000 times faster than Gensim. Magnitude introduces several novel features for improved robustness like out-of-vocabulary lookups. …

Rule Induction google
Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data. …

Semantic Role Labeling (SRL) google
In the field of artificial intelligence, Semantic role labeling, sometimes also called shallow semantic parsing, is a process in natural language processing that assigns labels to words or phrases in a sentence that indicate their semantic role in the sentence, such as that of an agent, goal, or result. It consists of the detection of the semantic arguments associated with the predicate or verb of a sentence and their classification into their specific roles. For example, given a sentence like ‘Mary sold the book to John’, the task would be to recognize the verb ‘to sell’ as representing the predicate, ‘Mary’ as representing the seller (agent), ‘the book’ as representing the goods (theme), and ‘John’ as representing the recipient. This is an important step towards making sense of the meaning of a sentence. A semantic analysis of this sort is at a lower-level of abstraction than a syntax tree, i.e. it has more categories, thus groups fewer clauses in each category. For instance, ‘the book belongs to me’ would need two labels such as ‘possessed’ and ‘possessor’ whereas ‘the book was sold to John’ would need two other labels such as ‘goal’ (or ‘theme’) and ‘receiver’ (or ‘recipient’) even though these two clauses would be very similar as far as ‘subject’ and ‘object’ functions are concerned.
Towards Semi-Supervised Learning for Deep Semantic Role Labeling