text: Analogy completion has been a popular task in recent years for evaluating the semantic properties of word embeddings, but the standard methodology makes a number of assumptions about analogies that do not always hold, either in recent benchmark datasets or when expanding into other domains. Through an analysis of analogies in the biomedical domain, we identify three assumptions: that of a Single Answer for any given analogy, that the pairs involved describe the Same Relationship, and that each pair is Informative with respect to the other. We propose modifying the standard methodology to relax these assumptions by allowing for multiple correct answers, reporting MAP and MRR in addition to accuracy, and using multiple example pairs. We further present BMASS, a novel dataset for evaluating linguistic regularities in biomedical embeddings, and demonstrate that the relationships described in the dataset pose significant semantic challenges to current word embedding methods.
  @cite_14: This paper proposes a method to acquire linguistic features from a corpus of short sentences by extracting analogous sentences like what ’s the next station ? : where ’s the bus station ? :: what is the next stop ? : where is the bus stop ? The procedures used to construct clusters of analogous sentences are presented. Experiments performed on roughly 40,000 short sentences from the tourism domain in English and Japanese are reported, and the clusters produced are analyzed and interpreted in terms of linguistic features.
  @cite_8: While classical approaches to unsupervised morphology acquisition often rely on metrics based on information theory for identifying morphemes, we describe a novel approach relying on the notion of formal analogy. A formal analogy is a relation between four forms, such as: reader is to doer as reading is to doing. Our assumption is that formal analogies identify pairs of morphologically related words. We first describe an approach which simply identifies all the formal analogies involving words in a lexicon. Despite its promising results, this approach is computationally too expensive. Therefore, we designed a more practical system which learns morphological structures using only a (small) subset of all formal analogies. We tested those two approaches on the five languages used in Morpho Challenge 2009.
  @cite_10: One can measure the extent to which a knowledge-base enables intelligent or creative behavior by determining how useful such a knowledge-base is to the solution of standard psychometric or scholastic tests. In this paper we consider the utility of WordNet, a comprehensive lexical knowledge-base of English word meanings, to the solution of S.A.T. analogies. We propose that these analogies test a student's ability to recognize and estimate a measure of pairwise analogical similarity, and describe an algorithmic formulation of this measure using the structure of WordNet. We report that the knowledge-based approach yields a precision at least equal to that of statistical machine-learning approaches.
  @cite_21: 
  @cite_1: 
  @cite_23: We present a language agnostic, unsupervised method for inducing morphological transformations between words. The method relies on certain regularities manifest in highdimensional vector spaces. We show that this method is capable of discovering a wide range of morphological rules, which in turn are used to build morphological analyzers. We evaluate this method across six different languages and nine datasets, and show significant improvements across all languages.
  @cite_13: Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenomenon; we need to seek a unified approach. We propose to subsume a broad range of phenomena under analogies. To limit the scope of this paper, we restrict our attention to the subsumption of synonyms, antonyms, and associations. We introduce a supervised corpus-based machine learning algorithm for classifying analogous word pairs, and we show that it can solve multiple-choice SAT analogy questions, TOEFL synonym questions, ESL synonym-antonym questions, and similar-associated-both questions from cognitive psychology.
  @cite_20: 
  @cite_11: We introduce a way to represent word pairs instantiating arbitrary semantic relations that keeps track of the contexts in which the words in the pair occur both together and independently. The resulting features are of sufficient generality to allow us, with the help of a standard supervised machine learning algorithm, to tackle a variety of unrelated semantic tasks with good results and almost no task-specific tailoring.
summary: Analogical reasoning has been studied both on its own and as a component of downstream tasks, using a range of systems. Early work used rule-based systems for world knowledge @cite_20 and syntactic @cite_1 relationships. Supervised models were used for SAT (Scholastic Aptitude Test) analogies @cite_10 , and later for synonymy, antonymy, and some world knowledge @cite_13 @cite_11 . Analogical reasoning has also been used in support of downstream tasks, including word sense disambiguation @cite_21 and morphological analysis @cite_14 @cite_8 @cite_23 .