Lexical co-occurrence is an important cue for detecting word associations. We present a theoretical framework for discovering statistically significant lexical co-occurrences from a given corpus. In contrast with the prevalent practice of giving weightage to unigram frequencies, we focus only on the documents containing both the terms (of a candidate bigram). We detect biases in span distributions of associated words, while being agnostic to variations in global unigram frequencies. Our framework has the fidelity to distinguish different classes of lexical co-occurrences, based on strengths of the document and corpuslevel cues of co-occurrence in the data. We perform extensive experiments on benchmark data sets to study the performance of various co-occurrence measures that are currently known in literature. We find that a relatively obscure measure called Ochiai, and a newly introduced measure CSA capture the notion of lexical co-occurrence best, followed next by LLR, Dice, and TTest, while another popular measure, PMI, suprisingly, performs poorly in the context of lexical co-occurrence.
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We present a neurosymbolic framework for the lifelong learning of algorithmic tasks that mix perception and procedural reasoning. Reusing high-level concepts across domains and learning complex procedures are key challenges in lifelong learning. We show that a program synthesis approach that combines gradient descent with combinatorial search over programs can be a more effective response to these challenges than purely neural methods. Our framework, called HOUDINI, represents neural networks as strongly typed, differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions. Our learning algorithm consists of: (1) a symbolic program synthesizer that performs a type-directed search over parameterized programs, and decides on the library functions to reuse, and the architectures to combine them, while learning a sequence of tasks; and (2) a neural module that trains these programs using stochastic gradient descent. We evaluate HOUDINI on three benchmarks that combine perception with the algorithmic tasks of counting, summing, and shortest-path computation. Our experiments show that HOUDINI transfers high-level concepts more effectively than traditional transfer learning and progressive neural networks, and that the typed representation of networks significantly accelerates the search.
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