New directions in the study of family names

This paper explores and explains recent radical developments in resources and methodology for studying the origins, cultural associations, and histories of family names (also called ‘surnames’). It summarizes the current state of the art and outlines new resources and procedures that are now becoming available. It shows how such innovations can enable the correction of errors in previous work and improve the accuracy of dictionaries of family names, with a focus on the English-speaking world. Developments such as the digitization of archives are having a profound effect, not only on the interpretation and understanding of traditional, ‘established’ family names and their histories, but also of names in other languages and other cultures. There are literally millions of different family names in the world today, many of which have never been studied at all. What are good criteria for selection of entries in a dictionary of family names, and what can be said about them? What is the nature of the evidence? How stable (or how variable) are family names over time? What are the effects of factors such as migration? What is the relationship between family names and geographical locations, given that people can and do move around? What is the relationship between traditional philological and historical approaches to the subject and statistical analysis of newly available digitized data? The paper aims to contribute to productive discussion of such questions

What lexical sets tell us about conceptual categories

It is common practice in computational linguistics to attempt to use selectional constraints and semantic type hierarchies as primary knowledge resources to perform word sense disambiguation (cf. Jurafsky and Martin 2000). The most widely adopted methodology is to start from a given ontology of types (e.g. Wordnet, cf. Miller and Fellbaum 2007) and try to use its implied conceptual categories to specify the combinatorial constraints on lexical items. Semantic Typing information about selectional preferences is then used to guide the induction of senses for both nouns and verbs in texts. Practical results have shown, however, that there are a number of problems with such an approach. For instance, as corpus-driven pattern analysis shows (cf. Hanks et al. 2007), the paradigmatic sets of words that populate specific argument slots within the same verb sense do not map neatly onto conceptual categories, as they often include words belonging to different types. Also, the internal composition of these sets changes from verb to verb, so that no stable generalization seems possible as to which lexemes belong to which semantic type (cf. Hanks and Jezek 2008). In this paper, we claim that these are not accidental facts related to the contingencies of a given ontology, but rather the result of an attempt to map distributional language behaviour onto semantic type systems that are not sufficiently grounded in real corpus data. We report the efforts done within the CPA project (cf. Hanks 2009) to build an ontology which satisfies such requirements and explore its advantages in terms of empirical validity over more speculative ontologies

Italian surnames in the Family Names of the United Kingdom project

An overview of surnames of Italian origin treated in the Family Names of the United Kingdom project

Wie man aus Wörtern Bedeutungen macht: Semantische Typen treffen Valenzen

Wie versteht ein Hörer oder Leser die von einem Sprecher oder Schreiber beabsichtigte Bedeutung? Syntaktische Strukturen sind zu allgemein, um feine Bedeutungsunterscheidungen auszudrücken. Wörter sind oft sehr mehrdeutig, und aufgrund dessen unzuverlässig als „Bedeutungsleitfaden“. Im Gegensatz dazu zeigt die Korpusmusteranalyse, dass die meisten Äußerungen aus Mustern von vergleichsweise geringer Mehrdeutigkeit aufgebaut sind. Daher stellt sich die Frage: Was ist ein Muster? Muster sind häufig verwendete Sprachbausteine, die aus zwei Elementen bestehen: Valenzen und Kollokationen. Während Valenzen relativ stabil sind, sind Kollokationen extrem variabel. In der Korpusmusteranalyse wird eine große Anzahl von Gebrauchsbelegen jedes Wortes studiert, und seine Kollokationen werden, ihren semantischen Typen entsprechend, lexikalischen Sets zugeordnet. Jedes Wort einer Sprache ist Bestandteil von mindestens einem Muster. Wenn es Teil von mehr als einem Muster ist, können die Bedeutungen seiner Muster meist durch unterschiedliche Kollokations-Präferenzen unterschieden werden. Kreative Benutzungen sind Abweichungen von normalen Nutzmustern, aber Abweichungen sind selbst regelgeleitet. Daher benötigt man eine Theorie von Normen und Abweichungen. Da die zwei Regelsysteme interagieren, können wir die Theorie als eine „Doppelhelix“ beschreiben

Three kinds of semantic resonance

This presentation suggests some reasons why lexicographers of the future will need to pay more attention to phraseology and non-literal meaning. It argues that not only do words have literal meaning, but also that much meaning is non-literal, being lexical, i.e. metaphorical or figurative, experiential, or intertextual.Published versio

The way to analyse ‘way’: A case study in word-specific local grammar

This is an accepted manuscript of an article published by Oxford Academic in International Journal of Lexicography on 11/02/2019, available online: https://doi.org/10.1093/ijl/ecz005 The accepted version of the publication may differ from the final published version.Traditionally, dictionaries are meaning-driven—that is, they list different senses (or supposed senses) of each word, but do not say much about the phraseology that distinguishes one sense from another. Grammars, on the other hand, are structure-driven: they attempt to describe all possible structures of a language, but say little about meaning, phraseology, or collocation. In both disciplines during the 20th century, the practice of inventing evidence rather than discovering it led to intermittent and unpredictable distortions of fact. Since 1987, attempts have been made in both lexicography (Cobuild) and syntactic theory (pattern grammar, construction grammar) to integrate meaning and phraseology. Corpora now provide empirical evidence on a large scale for lexicosyntactic description, but there is still a long way to go. Many cherished beliefs must be abandoned before a synthesis between empirical lexical analysis and grammatical theory can be achieved. In this paper, by empirical analysis of just one word (the noun way), we show how corpus evidence can be used to tackle the complexities of lexical and constructional meaning, providing new insights into the lexis-grammar interface

The Role of Corpus Pattern Analysis in Machine Translation Evaluation

This paper takes a preliminary look at the relation between verb pattern matches in the Pattern Dictionary of English Verbs (PDEV) and translation quality through a qualitative analysis of human-ranked sentences from 5 different machine translation systems. The purpose of the analysis is not only to determine whether verbs in the automatic translations and their immediate contexts match any pre-existing semanto-syntactic pattern in PDEV, but also to establish links between hypothesis sentences and the verbs in the reference translation. It attempts to answer the question of whether or not the semantic and syntactic information captured by Corpus Pattern Analysis (CPA) can indicate whether a sentence is a “good” translation. Two human annotators manually identified the occurrence of patterns in 50 translations and indicated whether these patterns match any identified pattern in the corresponding reference translation. Results indicate that CPA can be used to distinguish between well and ill-formed sentences

Corpus Patterns for Semantic Processing

This tutorial presents a corpus-driven, pattern-based empirical approach to meaning representation and computation. Patterns in text are everywhere, but techniques for identifying and processing them are still rudimentary. Patterns are not merely syntactic but syntagmatic: each pattern identifies a lexico-semantic clause structure consisting of a predicator (verb or predicative adjective) together with open-ended lexical sets of collocates in different clause roles (subject, object, prepositional argument, etc.). If NLP is to make progress in identifying and processing text meaning, pattern recognition and collocational analysis will play an essential role, because