Antonyms as lexical constructions: or, why paradigmatic construction is not an oxymoron

This paper argues that antonymy is a syntagmatic as well as a paradigmatic relation, and that antonym pairs constitute a particular type of construction. This position relies on three observations about antonymy in discourse: (1) antonyms tend to co-occur in sentences, (2) they tend to co-occur in particular contrastive constructions, and (3) unlike other paradigmatic relations, antonymy is lexical as well as semantic in nature. CxG offers a means to treat both the contrastive constructions and conventionalised antonym pairings as linguistic constructions, thus providing an account of how semantically paradigmatic relations come to be syntagmatically realised as well. After reviewing the relevant characteristics of CxG, it looks at some of the phrasal contexts in which antonyms tend to co-occur and argues that at least some of these constitute constructions with contrastive import. It then sketches a new type of discontinuous lexical construction that treats antonym pairs as lexical items, and raises issues for further discussion

Statistics for sentential co-occurrence

There is a growing trend in linguistics to use large corpora as a tool in the study of language. Through the investigation of the different contexts a word occurs in, it is possible to gain insight in the meanings associated with the word. Concordances are commonly used as a tool in lexicography, but while the study of concordances is fruitful it is also tedious, so statistical methods are gaining grounds in corpus linguistics. Several statistical measures have been introduced to measure the strength in association between two words, e.g. t-score (Barnbrook 1996:97-98), mutual information, MI (Charniak 1993; McEnery & Wilson 1996; Oakes 1998) and Berry-Rogghe’s z-score (1973). Those measures are designed to measure the strength of association between words occurring at a close distance from each other, i.e. immediately next to each other or within a fixed window span. Research that uses the sentence as a linguistic unit of study has also been presented. For example, antonymous concepts have been shown to co-occur in the same sentence more often than chance predicts by Justeson & Katz 1991, 1992 and Fellbaum 1995. A problem using the sentence as unit of study is that the lengths of the sentences vary from sentence to sentence. This has an impact on the statistical calculation – it is more likely to find two given words in a long sentence than in a short one. The probability of finding two given words co-occurring in the same sentence is thus affected. We introduce an exact expression for the calculation of the expected number of sentential co-occurrences. The p-value is calculated assuming that the number of random co-occurrences follows a Poisson distribution. A formal proof justifying this approximation is provided in the appendix. Apart from the statistical methods that account for the variation in sentence length, a case study is presented as an application of the statistical method. The study replicates Justeson and Katz’s 1991 study that shows that English antonyms co-occur sententially more frequently than chance predicts. The results of our study show that the variation in sentence length causes the chance for co-occurrence of two given words to increase. However, the main finding of Justeson & Katz is reinforced: antonyms co-occur significantly more often in the same sentence than expected by chance

Exploring the measurement of markedness and its relationship with other linguistic variables

Antonym pair members can be differentiated by each word's markedness-that distinction attributable to the presence or absence of features at morphological or semantic levels. Morphologically marked words incorporate their unmarked counterpart with additional morphs (e.g., "unlucky" vs. "lucky"); properties used to determine semantically marked words (e.g., "short" vs. "long") are less clearly defined. Despite extensive theoretical scrutiny, the lexical properties of markedness have received scant empirical study. The current paper employs an antonym sequencing approach to measure markedness: establishing markedness probabilities for individual words and evaluating their relationship with other lexical properties (e.g., length, frequency, valence). Regression analyses reveal that markedness probability is, as predicted, related to affixation and also strongly related to valence. Our results support the suggestion that antonym sequence is reflected in discourse, and further analysis demonstrates that markedness probabilities, derived from the antonym sequencing task, reflect the ordering of antonyms within natural language. In line with the Pollyanna Hypothesis, we argue that markedness is closely related to valence; language users demonstrate a tendency to present words evaluated positively ahead of those evaluated negatively if given the choice. Future research should consider the relationship of markedness and valence, and the influence of contextual information in determining which member of an antonym pair is marked or unmarked within discourse

Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network

Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees. In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. The results from classification experiments show that AntSynNET improves the performance over prior pattern-based methods.Comment: EACL 2017, 10 page

Taking antonymy mask off in vector space

Automatic detection of antonymy is an important task in Natural Language Processing (NLP) for Information Retrieval (IR), Ontology Learning (OL) and many other semantic applications. However, current unsupervised approaches to antonymy detection are still not fully effective because they cannot discriminate antonyms from synonyms. In this paper, we introduce APAnt, a new Average-Precision-based measure for the unsupervised discrimination of antonymy from synonymy using Distributional Semantic Models (DSMs). APAnt makes use of Average Precision to estimate the extent and salience of the intersection among the most descriptive contexts of two target words. Evaluation shows that the proposed method is able to distinguish antonyms and synonyms with high accuracy across different parts of speech, including nouns, adjectives and verbs. APAnt outperforms the vector cosine and a baseline model implementing the co-occurrence hypothesis

Prijedložna antonimija u hrvatskome: korpusni pristup

Prepositions as a word class pose various questions as to the relation between lexical and functional language units and their place in the lexicon (Jolly 1991, Šarić and Reindl 2001). Though often referred to as function words, prepositions show a) systematic semantic relations, ie. near–synonymy, polysemy, antonymy and b) a wide variety of lexical and functional (grammatical) uses, indicating a complex interplay of systematic features and contextual modifications which participate in the formation of their meaning. Semantic relations such as antonymy are mostly discussed in terms of adjectives, nouns and verbs, leaving out a detailed description of antonymy effects in other word classes such as prepositions (e.g. Lyons 1977, Cruse 1986, Jones et al. 2012). By adopting the methodology of antonymy research developed for identifying and extracting antonyms from corpora, we examine the co–occurrence of prepositional antonyms in the Croatian National Corpus. We take up the cognitive linguistic position of examining antonymy as a prototype based category based on both conceptual opposition and contextual modifications (Paradis et al. 2009), and we observe its workings on the novel prepositional dataset. Based on the primary domains and conceptual structures that motivate prepositional opposition formation, we divide the antonyms into spatial (directional and locational), temporal and non–dimensional types. For each of the antonym types there are different contextual modifications and conceptual structures that shape these antonymy relations, indicating a complex interplay between language system and language use.Prijedlozi kao vrsta riječi otvaraju mnoga pitanja o vezi leksičkih i funkcionalnih riječi i njihovu mjestu u leksikonu jezika (Jolly 1991, Šarić i Reindl 2001). Iako se često definiraju kao funkcionalne jedinice jezika, prijedlozi pokazuju: a) sustavne semantičke odnose, odnosno sinonimiju, polisemiju i antonimiju i b) veliku raznolikost njihovih leksičkih i funkcionalnih uporaba koja upućuje na složene odnose njihovih obilježja u jezičnom sustavu i kontekstualnih modifikacija koje sudjeluju u oblikovanju njihovih značenja. Međuleksički odnosi, poput antonimije, većinom se usredotočavaju na opise punoznačnih riječi poput imenica, pridjeva i glagola, izostavljajući sustavan opis antonimije u drugim vrstama riječi kao što su prijedlozi (e.g. Lyons 1977, Cruse 1986, Jones et al. 2012). Stoga je cilj rada ponuditi opis prijedložne antonimije koristeći se metodama razvijenim za identifikaciju antonima u korpusima, poglavito metode supojavljivanja antonima u različitim kontekstima. U skladu s kognitivnolingvističkim pristupom antonimija se definira kao prototipno ustrojena kategorija utemeljena na konceptualnim strukturama, kao i na kontekstualnim modifikacijama (Paradis i sur. 2009). Prijedložni antonimski parnjaci grupirani su u tri kategorije na temelju primarnih domena kojima pripadaju te konceptualnih struktura koje motiviraju razvoj njihovih opozicija, prostorni (direkcionalni i lokacijski), vremenski te nedimenzionalni antonimi. Za svaku kategoriju antonima raspravlja se o različitim konceptualnim strukturama kao temelju za uspostavu odnosa suprotnosti te kontekstnim modifikacijama koje utječu na ovaj međuleksički odnos. Antonimija se tako kao međuleksički odnos proučava s obzirom na složenosti unutarleksičkih, odnosno polisemnih struktura prijedloga, kao i sintagmatskih odnosa koji je odražavaju i motiviraju. Takav se međuodnos sintagmatskih i paradigmatskih odnosa promatra kao indikator složenih odnosa između jezičnog sustava i jezične uporabe