We describe a system for interactive modification of syntax trees by intuitive editing operations on the surface string. The system has a graphical interface, where the user can move, replace, add, and in other ways modify, words or phrases. During editing, the sentence is kept grammatical, by automatically rearranging words and changing inflection, if necessary. This is accomplished by combini...

This standard describes a syntax for private-key information. Private-key information includes a private key for some public-key cryptosystem and a set of attributes. The standard also describes a syntax for encrypted private keys. A password-based encryption algorithm (e.g., one of those described in PKCS #5 [1]) could be used to encrypt the private-key information. The intention of including ...

The Universal Stanford Dependencies (USD) subordinates function words to content words. Auxiliaries, adpositions and subordinators are positioned as dependents of full verbs and nouns, respectively. Such an approach to the syntax of natural languages is contrary to most work in theoretical syntax in the past 35 years, regardless of whether this work is constituencyor dependency-based. A substan...

How do humans learn the syntax and semantics of words from language experience? How does the mind discover abstract relationships between concepts? Computationalrelationships between concepts? Computational models of distributional semantics can analyze a corpus to derive representations of word meanings in terms of each word’s relationship to all other words in the corpus. While these models a...

This document describes the conventions for using several cryptographic algorithms with the Cryptographic Message Syntax (CMS) to protect the symmetric key package content type. Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at in effect on the date of publication of this document. Please review these documents carefully, as ...

The assignment of phrasal prominence has been variously attributed to syntactic structure, part speech, predictability, informativity, and speaker's intent. A recent account asserts that is memorized on a by-word basis as Accent Ratio (AR), the likelihood word accented (Nenkova et al. 2007). We examined whether AR outperforms traditional predictors, in particular syntax if not, predictors shed ...

Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly modeled. This work aims to answer two questions: 1) Does explicitly modeling target language syntax help NMT? 2) Is tight integration of words and syntax better than multitask tra...