Discourse relations and conjoined VPs: automated sense recognition

Sense classification of discourse relations is a sub-task of shallow discourse parsing. Discourse relations can occur both across sentences (inter-sentential) and within sentences (intra-sentential), and more than one discourse relation can hold between the same units. Using a newly available corpus of discourse-annotated intra-sentential conjoined verb phrases, we demonstrate a sequential classification system for their multi-label sense classification. We assess the importance of each feature used in the classification, the feature scope, and what is lost in moving from gold standard manual parses to the output of an off-the-shelf parser

Left Recursion in Parsing Expression Grammars

Parsing Expression Grammars (PEGs) are a formalism that can describe all deterministic context-free languages through a set of rules that specify a top-down parser for some language. PEGs are easy to use, and there are efficient implementations of PEG libraries in several programming languages. A frequently missed feature of PEGs is left recursion, which is commonly used in Context-Free Grammars (CFGs) to encode left-associative operations. We present a simple conservative extension to the semantics of PEGs that gives useful meaning to direct and indirect left-recursive rules, and show that our extensions make it easy to express left-recursive idioms from CFGs in PEGs, with similar results. We prove the conservativeness of these extensions, and also prove that they work with any left-recursive PEG. PEGs can also be compiled to programs in a low-level parsing machine. We present an extension to the semantics of the operations of this parsing machine that let it interpret left-recursive PEGs, and prove that this extension is correct with regards to our semantics for left-recursive PEGs.Comment: Extended version of the paper "Left Recursion in Parsing Expression Grammars", that was published on 2012 Brazilian Symposium on Programming Language

Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category

Table driven prediction for recursive descent LL(k) parsers

Programming languages are typically described in BNF or some extension of BNF, and the process of converting these descriptions into parsers is performed by parser generators. Some of the parser generators that convert LL grammars into parsers construct them to use recursive descent that gives them context during execution. The context is provided by the execution stack as the parser descends into the grammar and this is what allows the full expressiveness of LL grammars. Table driven parsers can be generated instead but restrictions are placed on the LL grammars that can be accepted. The benefit of tables is that they facilitate a separation of syntax analysis and semantic code written by a language designer. They are also faster and they simplify language implementation and modification. This paper proposes the possibility of a hybrid system that makes decisions using tables but once decisions are made recursive descent is employed to maintain context. The benefits of each system are maintained, and the drawbacks are mitigated. Also discussed are the modifications made to an existing parser generator, oops (version 2), so that it accepts LL(k) grammars and builds parsers using this system as proof-of-concept