Content word

Abstract: I begin with an intuition: when I read a sentence, I read it a chunk at a time. For example, the previous sentence breaks up something like this: (1) [I begin] [with an intuition]: [when I read] [a sentence], [I read it] [a chunk] [at a time] These chunks correspond in some way to prosodic patterns. It appears, for instance, that the strongest stresses in the sentence fall one to a chunk, and pauses are most likely to fall between chunks. Chunks also represent a grammatical watershed of sorts. The typical chunk consists of a single content word surrounded by a constellation of function words, matching a fixed template. A simple context-free grammar is quite adequate to describe the structure of chunks. By contrast, the relationships between chunks are mediated more by lexical selection than by rigid templates. Co-occurrence of chunks is determined not just by their syntactic categories, but is sensitive to the precise words that head them; and the order in which chunks occur is much more flexible than the order of words within chunks.

Abstract: The term word association is used in a very particular sense in the psycholinguistic literature. (Generally speaking, subjects respond quicker than normal to the word "nurse" if it follows a highly associated word such as "doctor.") We will extend the term to provide the basis for a statistical description of a variety of interesting linguistic phenomena, ranging from semantic relations of the doctor/nurse type (content word/content word) to lexico-syntactic co-occurrence constraints between verbs and prepositions (content word/function word). This paper will propose a new objective measure based on the information theoretic notion of mutual information, for estimating word association norms from computer readable corpora. (The standard method of obtaining word association norms, testing a few thousand subjects on a few hundred words, is both costly and unreliable.) The proposed measure, the association ratio, estimates word association norms directly from computer readable corpora, making it possible to estimate norms for tens of thousands of words.

Abstract: This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).