Sentiment polarity shifters : creating lexical resources through manual annotation and bootstrapped machine learning

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 focussed 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. Our most central step towards this is the creation of a large lexicon of polarity shifters that covers verbs, nouns and adjectives. To reduce the prohibitive cost of such a large annotation task, we develop a bootstrapping approach that combines automatic classification with human verification. This ensures the high quality of our lexicon while reducing annotation cost by over 70%. In designing the bootstrap classifier we develop a variety of features which use both existing semantic resources and linguistically informed text patterns. In addition we investigate how knowledge about polarity shifters might be shared across different parts of speech, highlighting both the potential and limitations of such an approach. The applicability of our bootstrapping approach extends beyond the creation of a single resource. We show how it can further be used to introduce polarity shifter resources for other languages. Through the example case of German we show that all our features are transferable to other languages. Keeping in mind the requirements of under-resourced languages, we also explore how well a classifier would do when relying only on data- but not resource-driven features. We also introduce ways to use cross-lingual information, leveraging the shifter resources we previously created for other languages. Apart from the general question of which words can be polarity shifters, we also explore a number of other factors. One of these is the matter of shifting directions, which indicates whether a shifter affects positive polarities, negative polarities or whether it can shift in either direction. Using a supervised classifier we add shifting direction information to our bootstrapped lexicon. For other aspects of polarity shifting, manual annotation is preferable to automatic classification. Not every word that can cause polarity shifting does so for every of its word senses. As word sense disambiguation technology is not robust enough to allow the automatic handling of such nuances, we manually create a complete sense-level annotation of verbal polarity shifters. To verify the usefulness of the lexica which we create, we provide an extrinsic evaluation in which we apply them to a sentiment analysis task. In this task the different lexica are not only compared amongst each other, but also against a state-of-the-art compositional polarity neural network classifier that has been shown to be able to implicitly learn the negating effect of negation words from a training corpus. However, we find that the same is not true for the far more lexically diverse polarity shifters. Instead, the use of the explicit knowledge provided by our shifter lexica brings clear gains in performance.Deutsche Forschungsgesellschaf

Defying chronology: : Crosslinguistic variation in reverse order reports

Much of how we sequence events in speech mirrors the order of their natural occurrence. While event chains that conform to chronology may be easier to process, languages offer substantial freedom to manipulate temporal order. This article explores to what extent digressions from chronology are attributable to differences in grammatical aspect systems. We compared reverse order reports (RORs) in event descriptions elicited from native speakers of four languages, two with (Spanish, Modern Standard Arabic [MSA]) and two without grammatical aspect (German, Hungarian). In the Arabic group, all participants were highly competent MSA speakers from Palestine and Jordan. Standardized frequency counts showed significantly more RORs expressed by non-aspect groups than by aspect groups. Adherence to chronology changing as a function of contrast in grammatical aspect signal that languages without obligatory marking of ongoingness may provide more flexibility for event reordering. These findings bring novel insights about the dynamic interplay between language structure and temporal sequencing in the discourse stream

Parentheticals and the dialogicity of signs

The term ‘parenthetical’ is applied to an almost unlimited range of linguistic phenomena, which share but one common feature, namely their being used parenthetically. Parenthetic use is mostly described in terms of embedding an expression into some host sentence. Actually, however, it is anything but clear what it means for an expression to be used parenthetically, from both a syntactic and a semantic point of view.Given that in most, if not all, cases the alleged host sentence can be considered syntactically and semantically complete in itself, it needs to be asked what kind of information the parenthetical contributes to the overall structure. Another issue to be addressed concerns the nature of the relation between parenthetical and host (explanation, question, etc.) and the question what is it that holds them together.Trying to figure out the basic function of parentheticals, the present paper proposes a semiotic analysis of parenthetically used expressions. This semiotic analysis is not intended to replace linguistic approaches, but is meant to elaborate on why parentheticals are so hard to capture linguistically. Taking a dynamic conception of signs and sign processes (in the sense of Peirce, Voloshinov and Bahtin) as starting point, parentheticals are argued to render explicit the inherent dialogicity of signs and utterances. This inherent dialogicity is hardly ever taken into consideration in linguistic analyses, which take the two-dimensional linearity of language as granted

A Type-coherent, Expressive Representation as an Initial Step to Language Understanding

A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference. Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable. This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs. ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution. This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations. We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers.Comment: Accepted for publication at The 13th International Conference on Computational Semantics (IWCS 2019

Opinion and Sentiment Analysis of Italian print press

As it is known, the success of a newspaper article for the public opinion can be measured by the degree in which the journalist is able to report and modify (if needed) attitudes, opinions, feelings and political beliefs. We present a symbolic system for Italian, derived from GETARUNS, which integrates a range of natural language processing tools with the intent to characterise the print press discourse. The system is multilingual and can produce deep text understanding. This has been done on some 500K words of text, extracted from three Italian newspaper in order to characterize their stance on a deep political crisis situation. We tried two different approaches: a lexicon-based approach for semantic polarity using off-the-shelf dictionaries with the addition of manually supervised domain related concepts; another one is a feature-based semantic and pragmatic approach, which computes propositional level analysis with the intent to better characterize important component like factuality and subjectivity. Results are quite revealing and confirm the otherwise common knowledge about the political stance of each newspaper on such topic as the change of government, that took placeatthe end of lastyear,2011

The Influences of the Celtic Languages on Present-Day English

This work surveys the state of research on contact influences of Celtic languages on English. Beginning with an overview of the main theories of language contact in general and their influence on the present problem, a historical framework is then laid out. Theories concerning historical language contact scenatios are presented. Possible contact features are discussed, ranging from Syntax and Morphology to Phonology and Loanwords