A global analysis of matches and mismatches between human genetic and linguistic histories

Human history is written in both our genes and our languages. The extent to which our biological and linguistic histories are congruent has been the subject of considerable debate, with clear examples of both matches and mismatches. To disentangle the patterns of demographic and cultural transmission, we need a global systematic assessment of matches and mismatches. Here, we assemble a genomic database (GeLaTo, or Genes and Languages Together) specifically curated to investigate genetic and linguistic diversity worldwide. We find that most populations in GeLaTo that speak languages of the same language family (i.e., that descend from the same ancestor language) are also genetically highly similar. However, we also identify nearly 20% mismatches in populations genetically close to linguistically unrelated groups. These mismatches, which occur within the time depth of known linguistic relatedness up to about 10,000 y, are scattered around the world, suggesting that they are a regular outcome in human history. Most mismatches result from populations shifting to the language of a neighboring population that is genetically different because of independent demographic histories. In line with the regularity of such shifts, we find that only half of the language families in GeLaTo are genetically more cohesive than expected under spatial autocorrelations. Moreover, the genetic and linguistic divergence times of population pairs match only rarely, with Indo-European standing out as the family with most matches in our sample. Together, our database and findings pave the way for systematically disentangling demographic and cultural history and for quantifying processes of shifts in language and social identities on a global scale

Syntactic identity, Parallelism and accommodated antecedents

Analyses of the ellipsis identity condition must account for the fact that some syntactic mismatches between an ellipsis site E and its antecedent A are possible while others are not. Previous accounts have suggested that the relevant distinction is between different kinds of heads, such that some heads in the ellipsis site may mismatch while others may not, and they have dealt with this sensitivity to a set of “special heads” with a built-for-purpose syntactic identity condition which holds over and above semantic identity to constrain ellipsis. In this article I argue against this approach and pursue an alternative which holds that identity is syntactic but “loose” in a precisely defined way. I show that the relevant generalization that accounts for syntactic identity effects in sluicing and VP-ellipsis-like constructions concerns the position of variables in the antecedent, rather than the feature content of syntactic heads. I propose an implementation of syntactic identity which allows for the accommodation of additional antecedents, with these being derived by a grammatical algorithm for generating alternatives, and I show that this implementation derives the right kinds of looseness while restricting mismatches with respect to the position of variables, thus deriving both the tolerable and intolerable mismatches between E and A without recourse to a specific condition regulating the content of special heads

Semantically Resolving Type Mismatches in Scientific Workflows

Scientists are increasingly utilizing Grids to manage large data sets and execute scientific experiments on distributed resources. Scientific workflows are used as means for modeling and enacting scientific experiments. Windows Workflow Foundation (WF) is a major component of Microsoft’s .NET technology which offers lightweight support for long-running workflows. It provides a comfortable graphical and programmatic environment for the development of extended BPEL-style workflows. WF’s visual features ease the syntactic composition of Web services into scientific workflows but do nothing to assure that information passed between services has consistent semantic types or representations or that deviant flows, errors and compensations are handled meaningfully. In this paper we introduce SAWSDL-compliant annotations for WF and use them with a semantic reasoner to guarantee semantic type correctness in scientific workflows. Examples from bioinformatics are presented

Translating near-synonyms: Possibilities and preferences in the interlingua

This paper argues that an interlingual representation must explicitly represent some parts of the meaning of a situation as possibilities (or preferences), not as necessary or definite components of meaning (or constraints). Possibilities enable the analysis and generation of nuance, something required for faithful translation. Furthermore, the representation of the meaning of words, especially of near-synonyms, is crucial, because it specifies which nuances words can convey in which contexts.Comment: 8 pages, LaTeX2e, 1 eps figure, uses colacl.sty, epsfig.sty, avm.sty, times.st

PP licensing in nominalizations

In this paper we compare the distribution of PPs introducing external arguments in nominalizations with PPs introducing external arguments in the verbal domain. We show that several mismatches exist between the behavior of PPs in nominalizations and PPs in the verbal domain. This leads us to suggest that while PPs in the verbal domain are licensed by functional structure alone, within the nominal domain, PPs can also be licensed via an interplay of the encyclopaedic meaning of the root involved and the properties of the preposition itself. This second mechanism kicks in in the absence of functional structure

If the Current Clique Algorithms are Optimal, so is Valiant's Parser

The CFG recognition problem is: given a context-free grammar $\mathcal{G}$ and a string $w$ of length $n$, decide if $w$ can be obtained from $\mathcal{G}$. This is the most basic parsing question and is a core computer science problem. Valiant's parser from 1975 solves the problem in $O(n^{\omega})$ time, where $\omega<2.373$ is the matrix multiplication exponent. Dozens of parsing algorithms have been proposed over the years, yet Valiant's upper bound remains unbeaten. The best combinatorial algorithms have mildly subcubic $O(n^3/\log^3{n})$ complexity. Lee (JACM'01) provided evidence that fast matrix multiplication is needed for CFG parsing, and that very efficient and practical algorithms might be hard or even impossible to obtain. Lee showed that any algorithm for a more general parsing problem with running time $O(|\mathcal{G}|\cdot n^{3-\varepsilon})$ can be converted into a surprising subcubic algorithm for Boolean Matrix Multiplication. Unfortunately, Lee's hardness result required that the grammar size be $|\mathcal{G}|=\Omega(n^6)$. Nothing was known for the more relevant case of constant size grammars. In this work, we prove that any improvement on Valiant's algorithm, even for constant size grammars, either in terms of runtime or by avoiding the inefficiencies of fast matrix multiplication, would imply a breakthrough algorithm for the $k$-Clique problem: given a graph on $n$ nodes, decide if there are $k$ that form a clique. Besides classifying the complexity of a fundamental problem, our reduction has led us to similar lower bounds for more modern and well-studied cubic time problems for which faster algorithms are highly desirable in practice: RNA Folding, a central problem in computational biology, and Dyck Language Edit Distance, answering an open question of Saha (FOCS'14)

In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology

This paper investigates the ability of neural network architectures to effectively learn diachronic phonological generalizations in a multilingual setting. We employ models using three different types of language embedding (dense, sigmoid, and straight-through). We find that the Straight-Through model outperforms the other two in terms of accuracy, but the Sigmoid model's language embeddings show the strongest agreement with the traditional subgrouping of the Slavic languages. We find that the Straight-Through model has learned coherent, semi-interpretable information about sound change, and outline directions for future research