Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing

Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in embeddings. This requires fine-grained analysis of embedding subspaces. Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding. The task we use is fine-grained name typing: given a large corpus, find all types that a name can refer to based on the name embedding. Given the scale of entities in knowledge bases, we can build datasets for this task that are complementary to the current embedding evaluation datasets in: they are very large, contain fine-grained classes, and allow the direct evaluation of embeddings without confounding factors like sentence contextComment: 6 pages, The 3rd Workshop on Representation Learning for NLP (RepL4NLP @ ACL2018

Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell

This paper presents an approach for investigating the nature of semantic information captured by word embeddings. We propose a method that extends an existing human-elicited semantic property dataset with gold negative examples using crowd judgments. Our experimental approach tests the ability of supervised classifiers to identify semantic features in word embedding vectors and com- pares this to a feature-identification method based on full vector cosine similarity. The idea behind this method is that properties identified by classifiers, but not through full vector comparison are captured by embeddings. Properties that cannot be identified by either method are not. Our results provide an initial indication that semantic properties relevant for the way entities interact (e.g. dangerous) are captured, while perceptual information (e.g. colors) is not represented. We conclude that, though preliminary, these results show that our method is suitable for identifying which properties are captured by embeddings.Comment: Accepted to the EMNLP workshop "Analyzing and interpreting neural networks for NLP

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Understanding Word Embedding Stability Across Languages and Applications

Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this thesis, we consider several aspects of embedding spaces, including their stability. First, we propose a definition of stability, and show that common English word embeddings are surprisingly unstable. We explore how properties of data, words, and algorithms relate to instability. We extend this work to approximately 100 world languages, considering how linguistic typology relates to stability. Additionally, we consider contextualized output embedding spaces. Using paraphrases, we explore properties and assumptions of BERT, a popular embedding algorithm. Second, we consider how stability and other word embedding properties affect tasks where embeddings are commonly used. We consider both word embeddings used as features in downstream applications and corpus-centered applications, where embeddings are used to study characteristics of language and individual writers. In addition to stability, we also consider other word embedding properties, specifically batching and curriculum learning, and how methodological choices made for these properties affect downstream tasks. Finally, we consider how knowledge of stability affects how we use word embeddings. Throughout this thesis, we discuss strategies to mitigate instability and provide analyses highlighting the strengths and weaknesses of word embeddings in different scenarios and languages. We show areas where more work is needed to improve embeddings, and we show where embeddings are already a strong tool.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162917/1/lburdick_1.pd