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Polish żeby under negation
(2021)
The paper addresses two patterns in the distribution of complement clauses headed by the complementizer żeby in Polish related to the presence of sentential negation. It is argued that żeby-clauses with an obligatory negation in the matrix clause, licensed by epistemic verbs, can be treated in terms of negative polarity, with żeby defined as an n-word. Structures with żeby-clauses and an obligatory negation in the embedded clause, licensed by verbs of fear, are argued to be an instance of negative complementation, with żeby specified as a negative complementizer. A uniform lexicalist analysis within the framework of HPSG is provided, employing tools developed to account for Negative Concord in Polish.
Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.
The sentiment polarity of an expression (whether it is perceived as positive, negative or neutral) can be influenced by a number of phenomena, foremost among them negation. Apart from closed-class negation words like no, not or without, negation can also be caused by so-called polarity shifters. These are content words, such as verbs, nouns or adjectives, that shift polarities in their opposite direction, e. g. abandoned in “abandoned hope” or alleviate in “alleviate pain”. Many polarity shifters can affect both positive and negative polar expressions, shifting them towards the opposing polarity. However, other shifters are restricted to a single shifting direction. Recoup shifts negative to positive in “recoup your losses”, but does not affect the positive polarity of fortune in “recoup a fortune”. Existing polarity shifter lexica only specify whether a word can, in general, cause shifting, but they do not specify when this is limited to one shifting direction. To address this issue we introduce a supervised classifier that determines the shifting direction of shifters. This classifier uses both resource-driven features, such as WordNet relations, and data-driven features like in-context polarity conflicts. Using this classifier we enhance the largest available polarity shifter lexicon.
As the nature of negative polarity items (NPIs) and their licensing contexts is still under much debate, a broad empirical basis is an important cornerstone to support further insights in this area of research. The work discussed in this paper is intended as a contribution to realizing this objective. The authors briefly introduce the phenomenon of NPIs and outline major theories about their licensing and also various licensing contexts before discussing our major topics: Firstly, a corpus-based retrieval method for NPI candidates is described that ranks the candidates according to their distributional dependence on the licensing contexts. Our method extracts single-word candidates and is extended to also capture multi-word candidates. The basic idea for automatically collecting NPI candidates from a large corpus is that an NPI behaves like a kind of collocate to its licensing contexts. Manual inspection and interpretation of the candidate lists identify the actual NPIs. Secondly, an online repository for NPIs and other items that show distributional idiosyncrasies is presented, which offers an empirical database for further (theoretical) research on these items in a sustainable way.
This paper presents three electronic collections of polarity items: (i) negative polarity items in Romanian, (ii) negative polarity items in German, and (iii) positive polarity items in German. The presented collections are a part of a linguistic resource on lexical units with highly idiosyncratic occurrence patterns. The motivation for collecting and documenting polarity items was to provide a solid empirical basis for linguistic investigations of these expressions. Our databe provides general information about the collected items, specifies their syntactic properties, and describes the environment that licenses a given item. For each licensing context, examples from various corpora and the Internet are introduced. Finally, the type of polarity (negative or positive) and the class (superstrong, strong, weak or open) associated with a given item is speci ed. Our database is encoded in XML and is available via the Internet, offering dynamic and exible access.