Russian Lexicographic Landscape: a Tale of 12 Dictionaries

The paper reports on quantitative analysis of 12 Russian dictionaries at three levels: 1) headwords: The size and overlap of word lists, coverage of large corpora, and presence of neologisms; 2) synonyms: Overlap of synsets in different dictionaries; 3) definitions: Distribution of definition lengths and numbers of senses, as well as textual similarity of same-headword definitions in different dictionaries. The total amount of data in the study is 805,900 dictionary entries, 892,900 definitions, and 84,500 synsets. The study reveals multiple connections and mutual influences between dictionaries, uncovers differences in modern electronic vs. traditional printed resources, as well as suggests directions for development of new and improvement of existing lexical semantic resources

Lexical typology : a programmatic sketch

The present paper is an attempt to lay the foundation for Lexical Typology as a new kind of linguistic typology.1 The goal of Lexical Typology is to investigate crosslinguistically significant patterns of interaction between lexicon and grammar

Bilingual Lexicography: Some Issues with Modern English Urdu Lexicography – a User's Perspective

The tradition of bilingual lexicography in the Indian subcontinent is more than two centuries old and goes back to as far as 1772 (Hadley). This article examines the development of bilingual lexicography in the Indian subcontinent with special reference to English-Hindustani or -Urdu dictionary development. It further explores some issues specific to this field and tries to suggest some solutions. First of all it describes the historical perspective of linguistic work in the subcontinent and then discusses issues relating to English-Urdu bilingual lexicography in particular

Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler

Specialized dictionaries are used to understand concepts in specific domains, especially where those concepts are not part of the general vocabulary, or having meanings that differ from ordinary languages. The first step in creating a specialized dictionary involves detecting the characteristic vocabulary of the domain in question. Classical methods for detecting this vocabulary involve gathering a domain corpus, calculating statistics on the terms found there, and then comparing these statistics to a background or general language corpus. Terms which are found significantly more often in the specialized corpus than in the background corpus are candidates for the characteristic vocabulary of the domain. Here we present two tools, a directed crawler, and a distributional semantics package, that can be used together, circumventing the need of a background corpus. Both tools are available on the web

Linguistically informed and corpus informed morphological analysis of Arabic

Standard English PoS-taggers generally involve tag-assignment (via dictionary-lookup etc) followed by tag-disambiguation (via a context model, e.g. PoS-ngrams or Brill transformations). We want to PoS-tag our Arabic Corpus, but evaluation of existing PoS-taggers has highlighted shortcomings; in particular, about a quarter of all word tokens are not assigned a fully correct morphological analysis. Tag-assignment is significantly more complex for Arabic. An Arabic lemmatiser program can extract the stem or root, but this is not enough for full PoS-tagging; words should be decomposed into five parts: proclitics, prefixes, stem or root, suffixes and postclitics. The morphological analyser should then add the appropriate linguistic information to each of these parts of the word; in effect, instead of a tag for a word, we need a subtag for each part (and possibly multiple subtags if there are multiple proclitics, prefixes, suffixes and postclitics). Many challenges face the implementation of Arabic morphology, the rich “root-and-pattern” nonconcatenative (or nonlinear) morphology and the highly complex word formation process of root and patterns, especially if one or two long vowels are part of the root letters. Moreover, the orthographic issues of Arabic such as short vowels ( َ ُ ِ ), Hamzah (ء أ إ ؤ ئ), Taa’ Marboutah ( ة ) and Ha’ ( ه ), Ya’ ( ي ) and Alif Maksorah( ى ) , Shaddah ( ّ ) or gemination, and Maddah ( آ ) or extension which is a compound letter of Hamzah and Alif ( أا ). Our morphological analyzer uses linguistic knowledge of the language as well as corpora to verify the linguistic information. To understand the problem, we started by analyzing fifteen established Arabic language dictionaries, to build a broad-coverage lexicon which contains not only roots and single words but also multi-word expressions, idioms, collocations requiring special part-of-speech assignment, and words with special part-of-speech tags. The next stage of research was a detailed analysis and classification of Arabic language roots to address the “tail” of hard cases for existing morphological analyzers, and analysis of the roots, word-root combinations and the coverage of each root category of the Qur’an and the word-root information stored in our lexicon. From authoritative Arabic grammar books, we extracted and generated comprehensive lists of affixes, clitics and patterns. These lists were then cross-checked by analyzing words of three corpora: the Qur’an, the Corpus of Contemporary Arabic and Penn Arabic Treebank (as well as our Lexicon, considered as a fourth cross-check corpus). We also developed a novel algorithm that generates the correct pattern of the words, which deals with the orthographic issues of the Arabic language and other word derivation issues, such as the elimination or substitution of root letters