There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval 2012 Task 2) and measuring compositional similarity between noun-modifier phrases and unigrams (multiple-choice paraphrase questions).
In SemEval 2012, Task 2 was concerned with measuring the degree of relational similarity between two word pairs @cite_13 and Task 6 @cite_25 examined the degree of semantic equivalence between two sentences. These two areas of research have been mostly independent, although and Turney present unified perspectives on the two tasks. We first discuss some work on relational similarity, then some work on compositional similarity, and lastly work that unifies the two types of similarity.
@cite_13 @cite_25
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Up to now, work on semantic relations has focused on relation classification: recognizing whether a given instance (a word pair such as virus:flu) belongs to a specific relation class (such as CAUSE:EFFECT). However, instances of a single relation class may still have significant variability in how characteristic they are of that class. We present a new SemEval task based on identifying the degree of prototypicality for instances within a given class. As a part of the task, we have assembled the first dataset of graded relational similarity ratings across 79 relation categories. Three teams submitted six systems, which were evaluated using two methods. Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two texts. This paper presents the results of the STS pilot task in Semeval. The training data contained 2000 sentence pairs from previously existing paraphrase datasets and machine translation evaluation resources. The test data also comprised 2000 sentences pairs for those datasets, plus two surprise datasets with 400 pairs from a different machine translation evaluation corpus and 750 pairs from a lexical resource mapping exercise. The similarity of pairs of sentences was rated on a 0-5 scale (low to high similarity) by human judges using Amazon Mechanical Turk, with high Pearson correlation scores, around 90 . 35 teams participated in the task, submitting 88 runs. The best results scored a Pearson correlation >80 , well above a simple lexical baseline that only scored a 31 correlation. This pilot task opens an exciting way ahead, although there are still open issues, specially the evaluation metric.