Towards interpretable, data-derived distributional meaning representations for reasoning: A dataset of properties and concepts (2019)

Abstract: This paper proposes a framework for investigating which types of semantic properties are represented by distributional data. The core of our framework consists of relations between concepts and properties. We provide hypotheses on which properties are reflected in distributional data or not based on the type of relation. We outline strategies for creating a dataset of positive and negative examples for various semantic properties, which cannot easily be separated on the basis of general similarity (e.g. fly: seagull, penguin). This way, a distributional model can only distinguish between positive and negative examples through evidence for a target property. Once completed, this dataset can be used to test our hypotheses and work towards data-derived interpretable representations.

## introduction when it comes to representations of word meaning, we currently have to choose between relatively transparent, interpretable representations that are low in coverage and opaque embedding representations with high coverage. while the former lend themselves well to reasoning, the latter are hard to interpret and their reasoning potential remains limited. ideally, we would have 'the best of both worlds': data-derived, high-coverage transparent representations we can reason over. reasoning over such vectors would open new opportunities for the study of phenomena at the core of lexical semantics, such as similarity and ambiguity (one form -multiple meanings) and variation (one meaning -multiple forms). in this paper, we present a framework for analyzing what type of semantic information is present in distributional data as a first step towards such semantic representations. we consider word meaning from the perspective of semantic properties, which enables us to explain semantic similarity and dissimilarity and reason over word meanings. we propose a methodology that can be used to create datasets representing concepts and their semantic properties, which can be used to test hypotheses about what type of information is present in distributional models. when trying to model the type of semantic information represented by linguistic context, the following questions arise: which aspects about the meaning of a word can be expected to be mentioned in (written) utterances? do people talk about the yellowness of lemons? or would they rather give accounts of what lemons are used for? we propose a number of hypotheses about which type of semantic knowledge is encoded in the linguistic context based on the semantic relation between a particular concept and property. if distributional vectors contain information about a semantic property, it should be possible to distinguish positive examples of the property from negative examples purely on the basis of the distributional vector. as distributional semantic representations usually provide good indications for general relatedness or similarity, one major pitfall of our approach is that words can easily be separated into positive and negative examples because they happen to fall into rather distinct categories. therefore, we specifically aim to collect challenging examples (e.g. fly: seagull, penguin rather than fly: seagull, table ). we propose a framework for sampling and defining concept-property pairs that, in future work, will be annotated and used to test our hypothesis. to the best of our knowledge, this will be the first dataset specifically designed to analyze the ability of embeddings to encode property information. besides being a diagnostic tool, we hope that the resulting resource will provide complementary information to traditional lexical semantic representations. a core notion in lexical semantics is semantic similarity. different lexical resources reflect this notion in different ways. whereas princeton wordnet @xcite structures semantic knowledge in terms of hierarchical categories, we approach similarity from the perspective of property overlap. implicitly, knowledge about property overlap is also represented in hierarchically structured categories, as they capture information about shared and distinguishing properties. we expect that the final dataset will be a complementary resource to wordnet as it could yield insights into semantic categorization in terms of semantic properties. currently, our setup only takes english data in consideration, but we think that valuable insights could be gained from extending it to more languages thus enabling cross-linguistic comparisons. the remainder of this paper is structured as follows: section 2 outlines insights on semantic properties from various research domains. based on this, we present a framework of properties and concepts in section 3, followed by our method for creating our dataset suitable for testing our hypotheses in section 4. we conclude and discuss the implications of our framework in section 5. ## theoretical background this section provides an overview of theories and observations about the type of knowledge encoded in linguistic contexts. in general, we assume that semantic information can either be encoded explicitly (e.g. by expressions such as lemons are yellow) or implicitly (e.g. the lemon rolled off the table, which indirectly indicates that lemons have a round shape). both sources of evidence provide sufficient information for humans to infer these properties. it is an open question to what extent this is represented by embedding models. we start this investigation by raising the question of what type of information is likely to be mentioned (either implicitly or explicitly) in natural language. different theoretical and applied fields have addressed this question, namely, language generation, corpus linguistics and cognitive theories of word meaning. we draw from approaches about referential expressions (section 2.1), typical properties and concepts revealed in similes (section 2.2) and afforded actions and processes (section 2.3). the remainder of this section provides an outline of these factors which form the basis of our proposed framework, introduced in section 3. ## contextually encoded properties based on the observation outlined in section 2, we present predictions about whether a specific distributional vector representation of a concept is likely to encode information about a specific semantic property or not. to operationalize this, we translate the factors discussed in section 2 to descriptions of relations between concepts and properties. we assume that knowledge about properties of concepts is generally implied and hence unlikely to be expressed explicitly (following the gricean maxim of quantity). however, there are a number of factors which cause violations against this general tendency. we translate these competing forces to relations between properties and concepts. we outline them below and summarize them in typicality. typical properties of instances of a concept are usually also highly implied (e.g. rosered). while a high level of impliedness in combination with gricean maxims would mean that the property is unlikely to be mentioned explicitly, typicality may have the opposite effect. based on the observations by @xcite , we expect that typical examples of a property can often serve to illustrate the property in a another concept (e.g. coal serves to illustrate blackness in the phrase eyes as black as coal, rose may serve to illustrate redness, etc). in contrast, properties that immediately come to mind when thinking of a concept, but not vice-versa are unlikely to be represented, but can be seen as highly implied (e.g. green is a typical property of broccoli, but broccoli is usually not used to illustrate greenness). affordedness. in general, we propose that afforded and usually performed activities are represented, while afforded and not usually performed activities are not (e.g. bowling ball -roll v.s. candle -roll). usually performed activities can be seen as highly implied knowledge about a concept. however, the fact that activities usually form part of specific events (which are not part of our implied knowledge) makes them much more likely to be mentioned in communication than other highly implied properties. in addition to being afforded properties themselves, activities can also provide indirect evidence for other properties. in particular, they provide indirect evidence for those properties which enable the activity. for instance, bowling balls are commonly involved in rollingactivities. the context is likely to provide direct evidence of the activity rolling (e.g. the bowling ball rolled by 5-foot-10). 2 the same evidence can also serve as an indirect indication for the property affording the rolling-activity, namely being round. many properties of a concept are, however, not necessarily reflected in activities. consider, for instance candles: even though they are often round (an affording property for the activity of rolling), rolling is not something they typically do. in the remainder of this paper, we use the following sub-types of properties: we distinguish activities from attributes. activities can be afforded and usually performed or afforded and not usually performed (or not afforded at all). attributes can fall under any of the relations outlined here. in addition, they can afford activities. variability. this factor refers to the degree of variation in instances of a concept. in general, we propose that variable properties are likely to be represented by linguistic contexts because they can be relevant for further distinctions and are not automatically implied. for instance, a color attribute can distinguish between different subcategories of bears or distinguish between peppers with different tastes, knives can be used for different cooking activities or processes, etc. these variable properties can have different degrees of discriminatory power. on one end of the spectrum, they distinguish between different conceptual categories (e.g. subcategories of bears). at the other end of the spectrum, they distinguish instances of the same category (e.g. t-shirts of different colors or dogs trained for different activities). while in this later case, there is a very high probability of properties to be mentioned explicitly, we do not expect the evidence to be enough to be captured by a distributional semantic model: due to the high degree of variance, individual properties will be mentioned sporadically at best. properties that can only apply to instances of concepts in exceptional cases are not expected to be represented. ## a dataset of concepts and properties this section describes the design of our dataset. we first outline the experiments we envision, because they provide the motivation of some of the key properties of our dataset. to conduct experiments on whether the predictions introduced in section 3 hold, we plan to use approaches suggested in the field of investigating neural network representations, such as diagnostic classification @xcite @xcite . in particular, we plan to extend the experiments presented in (accessed 2019/09/30) @xcite , which try to investigate whether dimensions of embedding representations can capture semantic properties. while this seems to be implied by the method of inferring the missing word in an analogy pair by means of vector subtraction and addition @xcite , analogy calculation methods have been heavily criticized, calling this notion into question @xcite @xcite . to shed light on this, we proposed an experimental set-up in which we tested whether a supervised machine learning system could successfully learn to distinguish vectors of words clearly associated with a property from vectors of words which are clearly not associated with the property. any supervised classification approach relies on finding regularities which are shared among all or most examples of a particular class and distinguish them from other classes. therefore, the distribution of positive and negative examples of properties is crucial to ensure that the vector dimensions discovered by the classifier actually correspond to the semantic property under investigation rather than some other information which happens to correlate with it. to illustrate the importance of the similarity distribution of positive and negative examples, consider the following: suppose our dataset for the property red consists of names of red fruits (positive examples) and green garden plants (negative examples). if we train and test a classifier on such a dataset, it is very likely that it can reach relatively high performance. but did it learn to identify the semantic property red in a distribution? in such a case, it would be impossible to draw a clear conclusion for the following reasons: the names of the red fruits most likely share more properties than being red, such as having a sweet taste, being used for similar things, or largely falling into the category of berries. consequently, more information connects these examples than the property red. the same holds for the negative examples: they belong to a relatively coherent category and probably share many properties. many of these properties will not be shared with the positive examples. this means that a classifier can rely on a multitude of indications, none of which are necessarily evidence of the target property red. figure 1 illustrates different scenarios of shared and distinguishing features. to address this challenge, our dataset has to adhere to the following requirements: most existing feature norm sets @xcite do not contain information about negative examples, as they only list (salient) properties of concepts. one might consider to derive negative examples by viewing all concepts not labeled with a certain feature as negative examples of the feature. this approach, however, results in a number of wrongly labeled instances, as positive cases are not always labeled as such (for instance, 18 out of 36 concepts labeled as is a bird are not labeled as is an animal in the cslb feature norms @xcite ). our main objective is to collect fine-grained information for property-concept pairs to fill this gap. through crowd annotations, we aim to divide these property-concept pairs into three categories: properties which apply to all or most, some or hardly any or no instances of a concept. we draw the line in the middle of the 'some' category, which encompasses different degrees of variability: while we expect attributes with little variance to have enough evidence for a model, attributes with a high degree of variability are most likely not encoded. the second requirement can be fulfilled by controlling (a) the selection of target properties (see section 4.1), (b) the selection of candidate concepts from resources (section 4.2) and (c) the selection of particularly challenging examples in the distributional semantic space (section 4.3). sections 4.4 and 4.5 provide further details on the setup of our crowd sourcing task. ## discussion and conclusion in this paper, we have outlined a method to create a dataset of semantic properties of concepts which can be used to evaluate whether and to what extent distributional models reflects semantic properties. this work can be positioned in our larger research goals, which involve creating transparent, interpretable lexical semantic representation in terms of semantic properties which lend themselves well for reasoning over ambiguity and variation. the dataset will be made available upon completion. 4 the main goal of this paper is to propose a design for a dataset that can be used to test the ability of word embeddings to represent semantic properties. a more precise understanding of what information word embeddings can provide is highly relevant for improving nlp systems relying on embeddings as lexical semantic representations. moreover, it can help in deciding whether embeddings are an appropriate representation in computational models of cognitive processes (as for instance discussed by @xcite ). eventually, we plan to move towards data-derived interpretable word representations in terms of semantic properties. the dataset proposed here enables us to use methods suggested in the area of studying representations and learning processes in neural networks, specifically diagnostic classification to test whether embeddings represent properties. in particular, we can go beyond the approach presented by @xcite , who use all concepts for which a property has not been elicited as negative examples of a property. in addition to proposing a dataset design, we offer specific hypotheses based on a variety of observations from different fields about information that is likely or unlikely to be expressed in english natural language corpora. rather than making claims based on entire categories of semantic properties, we base our predictions on underlying factors involved in the relations between concepts and properties. by testing these hypotheses, we hope to go beyond insights from experimental approaches comparing the information captured in embeddings to semantic feature norm sets (e.g. fagarasan et al. (2015), herbelot and vecchi (2015 finally, we hope that comparing the relations captured by our dataset to traditional, taxonomic categories represented in wordnet may yield insights about the relation between properties of concepts and categorization. this could be extended to other languages to enable crosslinguistic comparisons.