Distributional Measures of Semantic Distance: A Survey

The ability to mimic human notions of semantic distance has widespread applications. Some measures rely only on raw text (distributional measures) and some rely on knowledge sources such as WordNet. Although extensive studies have been performed to compare WordNet-based measures with human judgment, the use of distributional measures as proxies to estimate semantic distance has received little attention. Even though they have traditionally performed poorly when compared to WordNet-based measures, they lay claim to certain uniquely attractive features, such as their applicability in resource-poor languages and their ability to mimic both semantic similarity and semantic relatedness. Therefore, this paper presents a detailed study of distributional measures. Particular attention is paid to flesh out the strengths and limitations of both WordNet-based and distributional measures, and how distributional measures of distance can be brought more in line with human notions of semantic distance. We conclude with a brief discussion of recent work on hybrid measures

Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness

We propose and study a novel supervised approach to learning statistical semantic relatedness models from subjectively annotated training examples. The proposed semantic model consists of parameterized co-occurrence statistics associated with textual units of a large background knowledge corpus. We present an efficient algorithm for learning such semantic models from a training sample of relatedness preferences. Our method is corpus independent and can essentially rely on any sufficiently large (unstructured) collection of coherent texts. Moreover, the approach facilitates the fitting of semantic models for specific users or groups of users. We present the results of extensive range of experiments from small to large scale, indicating that the proposed method is effective and competitive with the state-of-the-art.Comment: 37 pages, 8 figures A short version of this paper was already published at ECML/PKDD 201

Improving approximation of domain-focused, corpus-based, lexical semantic relatedness

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches. Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track. The work presented in the dissertation indicates an improvement achieved w.r.t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work

Experiments on the difference between semantic similarity and relatedness

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), 81-88. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206

A distributional model of semantic context effects in lexical processinga

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›

The interaction between cognitive ease and informativeness shapes the lexicons of natural languages

Lexical ambiguity is pervasive in language, and often systematic. Previous work shows that systematic ambiguities involve related meanings. This is attributed to cognitive pressure towards simplicity in language, as it makes lexicons easier to learn and use. The present study examines the interplay between this pressure and competing pressure for languages to support accurate information transfer. We hypothesize that ambiguity is shaped by a balance of the two pressures; and find support for this idea in data from over 1200 languages and 1400 meanings. Our results thus suggest that universal forces shape the lexicons of natural languages

Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity