2015 AutoExtendExtendingWordEmbeddin

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Subject Headings: AutoExtend, Word Embedding.


Cited By



We present AutoExtend, a system to learn embeddings for synsets and lexemes. |It is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The synset/lexeme embeddings obtained live in the same vector space as the word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet as a lexical resource, but AutoExtend can be easily applied to other resources like Freebase. AutoExtend achieves state-of-the-art performance on word similarity and word sense disambiguation tasks.

1 Introduction

[[Unsupervised methods for word embeddings]] (also called “distributed word representations”) have become popular in natural language processing (NLP). These methods only need very large corpora as input to create sparse representations (e.g., based on local collocations) and project them into a lower dimensional dense vector space. Examples for word embeddings are SENNA (Collobert and Weston, 2008), the hierarchical log-bilinear model (Mnih and Hinton, 2009), word2vec (Mikolov et al., 2013c) and GloVe (Pennington et al., 2014). However, there are many other resources that are undoubtedly useful in NLP, including lexical resources like WordNet and Wiktionary and knowledge bases like Wikipedia and Freebase. We will simply call these resources in the rest of the paper. Our goal is to enrich these valuable resources with embeddings for those data types that are not words; e.g., we want to enrich WordNet with embeddings for synsets and lexemes. A synset is a set of synonyms that are interchangeable in some context. A lexeme pairs a particular spelling or pronunciation with a particular meaning, i.e., a lexeme is a conjunction of a word and a synset. Our premise is that many NLP applications will benefit if the non-word data types of resources – e.g., synsets in WordNet – are also available as embeddings. For example, in machine translation, enriching and improving translation dictionaries (cf. Mikolov et al. (2013b)) would benefit from these embeddings because they would enable us to create an enriched dictionary for word sensed. Generally, our premise is that the arguments for the utility of embeddings for word forms should carry over to the utility of embeddings for other data types like synsets in WordNet.

The insight underlying the method we propose is that the constraints of a resource can be formalized as constraints on embeddings and then allow us to extend word embeddings to embeddings of other data types like synsets. For example, the hyponymy relation in WordNet can be formalized as such a constraint.

The advantage of our approach is that it decouples embedding learning from the extension of embeddings to non-word data types in a resource. If somebody comes up with a better way of learning embeddings, these embeddings become immediately usable for resources. And we do not rely on any specific properties of embeddings that make them usable in some resources, but not in others.

An alternative to our approach is to train embeddings on annotated text, e.g., to train synset embeddings on corpora annotated with synsets. However, successful embedding learning generally requires very large corpora and sense labeling is too expensive to produce corpora of such a size.

Another alternative to our approach is to add up all word embedding vectors related to a particular node in a resource; e.g., to create the synset vector of lawsuit in WordNet, we can add the word vectors of the three words that are part of the synset (lawsuit, suit, case). We will call this approach naive and use it as a baseline (Snaive in Table 3).

We will focus on WordNet (Fellbaum, 1998) in this paper, but our method – based on a formalization that exploits the constraints of a resource for extending embeddings from words to other data types – is broadly applicable to other resources including Wikipedia and Freebase.

A word in WordNet can be viewed as a composition of several lexemes. Lexemes from different words together can form a synset. When a synset is given, it can be decomposed into its lexemes. And these lexemes then join to form words. These observations are the basis for the formalization of the constraints encoded in WordNet that will be presented in the next section: we view words as the sum of their lexemes and, analogously, synsets as the sum of their lexemes.

Another motivation for our formalization stems from the analogy calculus developed by Mikolov et al. (2013a), which can be viewed as a group theory formalization of word relations: we have a set of elements (our vectors) and an operation (addition) satisfying the properties of a mathematical group, in particular, associativity and invertibility. For example, you can take the vector of king, subtract the vector of man and add the vector of woman to get a vector near queen. In other words, you remove the properties of man and add the properties of woman. We can also see the vector of king as the sum of the vector of man and the vector of a gender-neutral ruler. The next thing to notice is that this does not only work for words that combine several properties, but also for words that combine several senses. The vector of suit can be seen as the sum of a vector representing lawsuit and a vector representing business suit. AutoExtend is designed to take word vectors as input and unravel the word vectors to the vectors of their lexemes. The lexeme vectors will then give us the synset vectors.

The main contributions of this paper are: (i) We present AutoExtend, a flexible method that extends word embeddings to embeddings of synsets and lexemes. AutoExtend is completely general in that it can be used for any set of embeddings and for any resource that imposes constraints of a certain type on the relationship between words and other data types. (ii) We show that AutoExtend achieves state-of-the-art word similarity and word sense disambiguation (WSD) performance. (iii) We publish the AutoExtend code for extending word embeddings to other data types, the lexeme and synset embeddings and the software to replicate our WSD evaluation.

This paper is structured as follows. Section 2 introduces the model, first as a general tensor formulation then as a matrix formulation making additional assumptions. In Section 3, we describe data, experiments and evaluation. We analyze Auto - Extend in Section 4 and give a short summary on how to extend our method to other resources in Section 5. Section 6 discusses related work.

2 Model

We are looking for a model that extends standard embeddings for words to embeddings for the other two data types in WordNet: synsets and lexemes. We want all three data types – words, lexemes, synsets – to live in the same embedding space. The basic premise of our model is: (i) words are sums of their lexemes and (ii) synsets are sums of their lexemes. We refer to these two premises as synset constraints. For example, the embedding of the word bloom is a sum of the embeddings of its two lexemes bloom (organ) and bloom (period); and the embedding of the synset flower-bloomblossom (organ) is a sum of the embeddings of its three lexemes flower (organ), bloom (organ) and blossom (organ).

The synset constraints can be argued to be the simplest possible relationship between the three WordNet data types. They can also be motivated by the way many embeddings are learned from corpora – for example, the counts in vector space models are additive, supporting the view of words as the sum of their senses. The same assumption is frequently made; for example, it underlies the group theory formalization of analogy discussed in Section 1.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 AutoExtendExtendingWordEmbeddinHinrich Schütze
Sascha Rothe
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes