AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis

Recently, sound recognition has been used to identify sounds, such as car and river. However, sounds have nuances that may be better described by adjective-noun pairs such as slow car, and verb-noun pairs such as flying insects, which are under explored. Therefore, in this work we investigate the relation between audio content and both adjective-noun pairs and verb-noun pairs. Due to the lack of datasets with these kinds of annotations, we collected and processed the AudioPairBank corpus consisting of a combined total of 1,123 pairs and over 33,000 audio files. One contribution is the previously unavailable documentation of the challenges and implications of collecting audio recordings with these type of labels. A second contribution is to show the degree of correlation between the audio content and the labels through sound recognition experiments, which yielded results of 70% accuracy, hence also providing a performance benchmark. The results and study in this paper encourage further exploration of the nuances in audio and are meant to complement similar research performed on images and text in multimedia analysis.Comment: This paper is a revised version of "AudioSentibank: Large-scale Semantic Ontology of Acoustic Concepts for Audio Content Analysis

Lexical relatedness and the lexical entry - a formal unification

Based on the notion of a lexicon with default inheritance, I address the problem of how to provide a template for lexical representations that allows us to capture the relatedness between inflected word forms and canonically derived lexemes within a broadly realizational-inferential model of morphology. To achieve this we need to be able to represent a whole host of intermediate types of lexical relatedness that are much less frequently discussed in the literature. These include transpositions such as deverbal participles, in which a word's morphosyntactic class changes (e.g. verb ⇒ adjective) but no semantic predicate is added to the semantic representation and the derived word remains, in an important sense, a "form" of the base lexeme (e.g. the 'present participle form of the verb'). I propose a model in which morphological properties are inherited by default from syntactic properties and syntactic properties are inherited from semantic properties, such as ontological category (the Default Cascade). Relatedness is defined in terms of a Generalized Paradigm Function (perhaps in reality a relation), a generalization of the Paradigm Function of Paradigm Function Morphology (Stump 2001). The GPF has four components which deliver respectively specifications of a morphological form, syntactic properties, semantic representation and a lexemic index (LI) unique to each individuated lexeme in the lexicon. In principle, therefore, the same function delivers derived lexemes as inflected forms. In order to ensure that a newly derived lexeme of a distinct word class can be inflected I assume two additional principles. First, I assume an Inflectional Specifiability Principle, which states that the form component of the GPF (which defines inflected word forms of a lexeme) is dependent on the specification of the lexeme's morpholexical signature, a declaration of the properties that the lexeme is obliged to inflect for (defined by default on the basis of morpholexical class). I then propose a Category Erasure Principle, which states that 'lower' attributes are erased when the GPF introduces a non-trivial change to a 'higher' attribute (e.g. a change to the semantic representation entails erasure of syntactic and morphological information). The required information is then provided by the Default Cascade, unless overridden by specific declarations in the GPF. I show how this model can account for a variety of intermediate types of relatedness which cannot easily be treated as either inflection or derivation, and conclude with a detailed illustration of how the system applies to a particularly interesting type of transposition in the Samoyedic language Sel'kup, in which a noun is transposed to a similitudinal adjective whose form is in paradigmatic opposition to case-marked noun forms, and which is therefore a kind of inflection

Classifying Crises-Information Relevancy with Semantics

Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis

Semantic Types, Lexical Sorts and Classifiers

We propose a cognitively and linguistically motivated set of sorts for lexical semantics in a compositional setting: the classifiers in languages that do have such pronouns. These sorts are needed to include lexical considerations in a semantical analyser such as Boxer or Grail. Indeed, all proposed lexical extensions of usual Montague semantics to model restriction of selection, felicitous and infelicitous copredication require a rich and refined type system whose base types are the lexical sorts, the basis of the many-sorted logic in which semantical representations of sentences are stated. However, none of those approaches define precisely the actual base types or sorts to be used in the lexicon. In this article, we shall discuss some of the options commonly adopted by researchers in formal lexical semantics, and defend the view that classifiers in the languages which have such pronouns are an appealing solution, both linguistically and cognitively motivated