Why is penguin more similar to polar bear than to sea gull? Analyzing conceptual knowledge in distributional models (2020)

Abstract: What do powerful models of word mean- ing created from distributional data (e.g. Word2vec (Mikolov et al., 2013) BERT (Devlin et al., 2019) and ELMO (Peters et al., 2018)) represent? What causes words to be similar in the semantic space? What type of information is lacking? This thesis proposal presents a framework for investigating the information encoded in distributional semantic models. Several analysis methods have been suggested, but they have been shown to be limited and are not well understood. This approach pairs observations made on actual corpora with insights obtained from data manipulation experiments. The expected outcome is a better understanding of (1) the semantic information we can infer purely based on linguistic co-occurrence patterns and (2) the potential of distributional semantic models to pick up linguistic evidence.

## introduction distributional semantic representations capture semantic similarity and relatedness and, perhaps more importantly, enable machine learning-based natural language processing models to abstract over lexical representations. but what type of semantic information do they contain? could distributional models show that the concepts lemon and moon share shape and color, but differ with respect to almost everything else? understanding what semantic knowledge is represented in embeddings can not only help us improve those representations but also shed light on questions about lexical representation raised in cognitive linguistics (e.g. the suitability of embeddings for models of metaphor interpretation @xcite ). understanding the way components of meaning are represented could eventually enable us to use data-derived, distributional representations for lexical reasoning. while exiting model analysis methods @xcite @xcite have yielded initial insights, they are still limited when applied to distributional word representations. gaining insights into semantic representations derived from massive amounts of textual data thus entails answering two core questions: ( 1 ) what information about concepts can we find in the linguistic data and how does it relate to people's knowledge about concepts? (2) what linguistic information in the data can be picked up by a distributional semantic model and how is it represented? answering these questions entails the following four steps: the core questions of this research proposal and their interaction are illustrated in figure 1 . the remainder of this paper is structured as follows: after discussing related work in section 2, i present linguistic hypotheses in section 3. the corpus of human judgments of property-concept pairs for testing these hypotheses is presented in section 4. section 5 outlines model analysis methods and simulation experiments, followed by a conclusion and reflection on possible outcomes in section 6. ## related work several studies investigate the relation between semantic features recorded in feature norm datasets @xcite @xcite and embedding vectors @xcite @xcite @xcite @xcite @xcite . these studies indicate that (at least partial) mappings between distributional and conceptual spaces are possible and that conceptual knowledge can complement distributional representations. @xcite shows that distributional similarity can indicate propertyoverlap. @xcite show that attributes of the type of knowledge recorded in knowledge bases can, to some extent, be learned from word embeddings. @xcite hypothesizes that gricean maxims determine what is mentioned in text, based on limited datasets. these studies provide partial evidence for conceptual knowledge in distributional data, but they do not provide a systematic account of the underlying factors at play. a major reason for this gap is the difficulty of interpreting representations resulting from ma-chine learning models. diagnostic classification has proven successful in the analysis of such representations @xcite and word embedding representations @xcite @xcite . however, the results of these experiments provide limited insights. unverified negative examples. for instance, in the cslb norms @xcite , has legs is listed for several birds, but not for owl, duck, and eagle. this introduces noise to already rather small datasets used to investigate property knowledge in distributional data @xcite . @xcite apply diagnostic classification to investigate semantic classes using a large, automatically generated dataset derived from wikipedia, which is likely to contain noise. sommerauer and fokkens (2018) and @xcite have provided small sets of verified examples to combat this issue. ## linguistic hypotheses this sections presents hypotheses about (a) what aspects of conceptual information people mention in texts (section 3.1) and (b) how they mention it (section 3.2). ## dataset design and crowd annotation the dataset for this thesis should contain conceptproperty pairs annotated with the fine-grained semantic relations introduced in section 3.1. the dataset should contain (1) enough positive and negative examples of a property to allow for diagnostic experiments and (2) positive and negative examples which cannot easily be separated based on general similarity in the semantic space @xcite . to address these aspects, the property-concept pairs were collected following the strategy outlined in @xcite . firstly, properties which are expected to apply to concepts across different semantic categories were selected (e.g. colors). secondly, existing resources (the cslb feature norms @xcite (an extended and improved version of the norms collected by @xcite ), but also wordnet @xcite , conceptnet @xcite and stereotype data @xcite were used to collect positive and negative example candidates for these properties. where possible, candidates were selected from diverse semantic categories. the candidates were extended by using a large-scale distributional model (googlenews word2vec model foot_2 ). the candidate pairs are labeled with semantic relations in a crowd task. crowd workers are presented with natural language statements about a specific pair illustrating a semantic relation and asked to indicate whether they agree or disagree. 3 test runs indicate that workers can complete around 70 questions in about 10 minutes. 4 each property-concept pair should have at least one relation which is perceived as appropriate by most participants (and is thus labeled with 'agree'). 5 however, it has been shown that ambiguity is inherent to many semantic annotation tasks @xcite , leading to disagreements. disagreement in this task is likely to arise . from two factors: (1) ambiguity in the interpretation of the concept, property, relation or combination and (2) different levels of knowledge about the world. disagreement caused by interpretation differences is particularly relevant for this dataset, as this is can indicate polysemy, which has been shown to have an impact on embedding representations @xcite . it is, however, still an open question how exactly it relates to the representation of semantic properties. ## method various analysis methods have been suggested to interpret latent representations resulting from machine learning (particularly deep learning) models. while they have yielded important insights, they still struggle with a number of limitations @xcite . i plan to approach these limitations by pairing analysis methods (described in section 5.1) with data simulation experiments (described in section 5.2). this combination is expected to yield insights into (1) the analysis methods and their potential and (2) the representation of linguistic evidence in a text corpus in distributional models. ## conclusion this proposal presents a framework for investigating the semantic content of distributional word representations from two perspectives: firstly, i propose to test linguistic hypotheses about what aspects of conceptual knowledge are represented in natural language. secondly, i propose to interpret the results against the background of a methodological investigation of model analysis methods and the potential of distributional models. the linguistic hypotheses to be tested may be falsified. while this would be a negative result, it is still a relevant insight and can be used as a basis for new predictions. furthermore, it can be expected that the methodological insights gained in the simulation experiments can inform other approaches investigating non-transparent embedding representations and yield important insights about the behavior of distributional models. i expect that the corpus and insights gathered in this project can be complementary to resources capturing common-sense knowledge explicitly, such as conceptnet @xcite and common sense challenges (e.g. @xcite ).