Reconstructing Native Language Typology from Foreign Language Usage

Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native language properties on linguistic performance in a foreign language. In this work we provide empirical evidence for this process in the form of a strong correlation between language similarities derived from structural features in English as Second Language (ESL) texts and equivalent similarities obtained from the typological features of the native languages. We leverage this finding to recover native language typological similarity structure directly from ESL text, and perform prediction of typological features in an unsupervised fashion with respect to the target languages. Our method achieves 72.2% accuracy on the typology prediction task, a result that is highly competitive with equivalent methods that rely on typological resources.Comment: CoNLL 201

Measuring Large Scale Space Perception in Literary Texts

The center and radius of perception associated with a written text are defined, and algorithms for their computation are presented. Indicators for anisotropy in large scale space perception are introduced. The relevance of these notions for the analysis of literary and historical records is briefly discussed and illustrated with an example taken from medieval historiography.Comment: 8 pages, 1 figur

Ultrametric embedding: application to data fingerprinting and to fast data clustering

We begin with pervasive ultrametricity due to high dimensionality and/or spatial sparsity. How extent or degree of ultrametricity can be quantified leads us to the discussion of varied practical cases when ultrametricity can be partially or locally present in data. We show how the ultrametricity can be assessed in text or document collections, and in time series signals. An aspect of importance here is that to draw benefit from this perspective the data may need to be recoded. Such data recoding can also be powerful in proximity searching, as we will show, where the data is embedded globally and not locally in an ultrametric space.Comment: 14 pages, 1 figure. New content and modified title compared to the 19 May 2006 versio

A validity study of the Flesch readability formula applied to mathematic materials

Thesis (Ed.M.)--Boston Universit

Using latent-semantic analysis and network analysis for monitoring conceptual development

This paper describes and evaluates CONSPECT (from concept inspection), an application that analyses states in a learner’s conceptual development. It was designed to help online learners and their tutors monitor conceptual development and also to help reduce the workload of tutors monitoring a learner’s conceptual development. CONSPECT combines two technologies - Latent Semantic Analysis (LSA) and Network Analysis (NA) into a technique called Meaningful Interaction Analysis (MIA). LSA analyses the meaning in the textual digital traces left behind by learners in their learning journey; NA provides the analytic instrument to investigate (visually) the semantic structures identified by LSA. This paper describes the validation activities undertaken to show how well LSA matches first year medical students in 1) grouping similar concepts and 2) annotating text

A network model of interpersonal alignment in dialog

In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi