While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding. Such strategies may not be optimal, however, as they are limited by the coverage of available resources and conflate similarity with other forms of relatedness. As an alternative, in this paper we propose to encode relational knowledge in a separate word embedding, which is aimed to be complementary to a given standard word embedding. This relational word embedding is still learned from co-occurrence statistics, and can thus be used even when no external knowledge base is available. Our analysis shows that relational word vectors do indeed capture information that is complementary to what is encoded in standard word embeddings.
A number of approaches have been proposed that are aimed at learning relation vectors for a given set of word pairs ( @math , @math ), based on sentences in which these word pairs co-occur. For instance, introduced a method called Latent Relational Analysis (LRA), which relies on first identifying a set of sufficiently frequent lexical patterns and then constructs a matrix which encodes for each considered word pair ( @math , @math ) how frequently each pattern @math appears in between @math and @math in sentences that contain both words. Relation vectors are then obtained using singular value decomposition. More recently, proposed an approach inspired by the GloVe word embedding model @cite_22 to learn relation vectors based on co-occurrence statistics between the target word pair @math and other words. Along similar lines, learn relation vectors based on the distribution of words occurring in sentences that contain @math and @math by averaging the word vectors of these co-occurring words. Then, a conditional autoencoder is used to obtain lower-dimensional relation vectors.
@cite_22
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There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This article introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47 on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) The patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56 on the 374 analogy questions, statistically equivalent to the average human score of 57 . On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.