Uniform Density in Linguistic Information derived from Dependency Structures

posted 10 months ago in #ICAART, #NLPinAI, #publications

This pilot study addresses the question of whether the Uniform Information Density principle (UID) can be proved for eight typologically diverse languages. The lexical information of words is derived from dependency structures both in sentences preceding the sentences and within the sentence in which the target word occurs. Dependency structures are a realisation of extra-sentential contexts for deriving information as formulated in the surprisal model. Only subject, object and oblique, i.e., the level directly below the verbal root node, were considered. UID says that in natural language, the variance of information and information jumps from word to word should be small so as not to make the processing of a linguistic message an insurmountable hurdle. We observed cross-linguistically different information distributions but an almost identical UID, which provides evidence for the UID hypothesis and assumes that dependency structures can function as proxies for extra-sentential contexts. However, for the dependency structures chosen as contexts, the information distributions in some languages were not statistically significantly different from distributions from a random corpus. This might be an effect of too low complexity of our model's dependency structures, so lower hierarchical levels (e.g. phrases) should be considered.

BibTex | DOI: 10.5220/0000155600003116

Keyword extraction in German: Information-theory vs. deep learning

posted 2 years, 9 months ago in #ICAART, #NLPinAI, #publications

This paper reports the results of a study on automatic keyword extraction in German. We employed in general two types of methods: (A) an unsupervised method based on information theory (Shannon, 1948). We employed (i) a bigram model, (ii) a probabilistic parser model (Hale, 2001) and (iii) an innovative model which utilises topics as extra-sentential contexts for the calculation of the information content of the words, and (B) a supervised method employing a recurrent neural network (RNN). As baselines, we employed TextRank and the TF-IDF ranking function. The topic model (A)(iii) outperformed clearly all remaining models, even TextRank and TF-IDF. In contrast, RNN performed poorly. We take the results as first evidence, that (i) information content can be employed for keyword extraction tasks and has thus a clear correspondence to semantics of natural language’s, and (ii) that - as a cognitive principle - the information content of words is determined from extra-sentential contexts, that is to say, from the discourse of words.

BibTex | DOI: 10.5220/0009374704590464