Semantic similarity

Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between them is based on the likeness of their meaning or semantic content as opposed to similarity which can be estimated regarding their syntactical representation (e.g. their string format). These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature.^[1] The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations.^[2] For example, "car" is similar to "bus", but is also related to "road" and "driving".

Computationally, semantic similarity can be estimated by defining a topological similarity, by using ontologies to define the distance between terms/concepts. For example, a naive metric for the comparison of concepts ordered in a partially ordered set and represented as nodes of a directed acyclic graph (e.g., a taxonomy), would be the shortest-path linking the two concept nodes. Based on text analyses, semantic relatedness between units of language (e.g., words, sentences) can also be estimated using statistical means such as a vector space model to correlate words and textual contexts from a suitable text corpus.

Terminology

The concept of semantic similarity is more specific than semantic relatedness, as the latter includes concepts as antonymy and meronymy, while similarity does not.^[3] However, much of the literature uses these terms interchangeably, along with terms like semantic distance. In essence, semantic similarity, semantic distance, and semantic relatedness all mean, "How much does term A have to do with term B?" The answer to this question is usually a number between -1 and 1, or between 0 and 1, where 1 signifies extremely high similarity.

Visualization

An intuitive way of visualizing the semantic similarity of terms is by grouping together terms which are closely related and spacing wider apart the ones which are distantly related. This is also common in practice for mind maps and concept maps and is sometimes subconscious.

Applications

Biomedical informatics

Semantic similarity measures have been applied and developed in biomedical ontologies,^[4][5][6] namely, the Gene Ontology (GO).^{[7][8][9][10]} They are mainly used to compare genes and proteins based on the similarity of their functions rather than on their sequence similarity, but they are also being extended to other bioentities, such as chemical compounds,^[11] anatomical entities^[12] and diseases.^[13]

These comparisons can be done using tools freely available on the web:

• ProteInOn can be used to find interacting proteins, find assigned GO terms and calculate the functional semantic similarity of UniProt proteins and to get the information content and calculate the functional semantic similarity of GO terms.^[14]
• CMPSim provides a functional similarity measure between chemical compounds and metabolic pathways using ChEBI based semantic similarity measures.^[15]
• CESSM provides a tool for the automated evaluation of GO-based semantic similarity measures.^[16]

GeoInformatics

Similarity is also applied to find similar geographic features or feature types:^[17]

• SIM-DL similarity server^[18] can be used to compute similarities between concepts stored in geographic feature type ontologies.
• Similarity Calculator can be used to compute how well related two geographic concepts are in the Geo-Net-PT ontology.^[19][20]
• The OSM Semantic Network can be used to compute the semantic similarity of tags in OpenStreetMap.^[21]

Computational linguistics

Several metrics use WordNet, a manually constructed lexical database of English words. Despite the advantages of having human supervision in constructing the database, since the words are not automatically learned the database cannot measure relatedness between multi-word term, non-incremental vocabulary.^[3][22]

Natural language processing

Natural language processing (NLP) is a field of computer science and linguistics. Sentiment analysis, Natural language understanding and Machine translation (Automatically translate text from one human language to another) are a few of the major areas where it is being used. For example, knowing one information resource in the internet, it is often of immediate interest to find similar resources. The Semantic Web provides semantic extensions to find similar data by content and not just by arbitrary descriptors.^{[23][24][25][26] [27] [28] [29] [30] [31]}

Measures

Topological similarity

There are essentially two types of approaches that calculate topological similarity between ontological concepts:

• Edge-based: which use the edges and their types as the data source;
• Node-based: in which the main data sources are the nodes and their properties.

Other measures calculate the similarity between ontological instances:

• Pairwise: measure functional similarity between two instances by combining the semantic similarities of the concepts they represent
• Groupwise: calculate the similarity directly not combining the semantic similarities of the concepts they represent

Some examples:

Edge-based

• Pekar et al.^[32]
• Cheng and Cline^[33]
• Wu et al.^[34]
• Del Pozo et al.^[35]
• IntelliGO: Benabderrahmane et al.^[6]

Node-based

• Resnik ^[36]
  • based on the notion of information content. The information content of a concept (term or word) is the logarithm of the probability of finding the concept in a given corpus.
  • only considers the information content of lowest common subsumer (lcs). A lowest common subsumer is a concept in a lexical taxonomy ( e.g. WordNet), which has the shortest distance from the two concepts compared. For example, animal and mammal both are the subsumers of cat and dog, but mammal is lower subsumer than animal for them.
• Lin ^[37]
  • based on Resnik's similarity.
  • considers the information content of lowest common subsumer (lcs) and the two compared concepts.
• Maguitman, Menczer, Roinestad and Vespignani ^[38]
  • Generalizes Lin's similarity to arbitrary ontologies (graphs).
• Jiang and Conrath ^[39]
  • based on Resnik's similarity.
  • considers the information content of lowest common subsumer (lcs) and the two compared concepts to calculate the distance between the two concepts. The distance is later used in computing the similarity measure.
• DiShIn Disjunctive Shared Information between Ontology Concepts ^[40]
  • other alternative: GraSM (Graph-based Similarity Measure) ^[41]
• Align, Disambiguate, and Walk: Random walks on Semantic Networks ^[42]

Node-and-Relation-Content-based

• applicable to ontology
• consider properties (content) of nodes
• consider types (content) of relations
• based on eTVSM^[43]
• based on Resnik's similarity ^[44]

Pairwise

• maximum of the pairwise similarities
• composite average in which only the best-matching pairs are considered (best-match average)

Groupwise

• Jaccard index
• simGIC ^[45]
• simLP
• simUI

Statistical similarity

Statistical similarity approaches can be learned from data, or predefined. Similarity learning can often outperform predefined similarity measures. Broadly speaking, these approaches build a statistical model of documents, and use it to estate similarity.

• LSA (Latent semantic analysis) ^[46][47](+) vector-based, adds vectors to measure multi-word terms; (−) non-incremental vocabulary, long pre-processing times
• PMI (Pointwise mutual information) (+) large vocab, because it uses any search engine (like Google); (−) cannot measure relatedness between whole sentences or documents
• SOC-PMI (Second-order co-occurrence pointwise mutual information) (+) sort lists of important neighbor words from a large corpus; (−) cannot measure relatedness between whole sentences or documents
• GLSA (Generalized Latent Semantic Analysis) (+) vector-based, adds vectors to measure multi-word terms; (−) non-incremental vocabulary, long pre-processing times
• ICAN (Incremental Construction of an Associative Network) (+) incremental, network-based measure, good for spreading activation, accounts for second-order relatedness; (−) cannot measure relatedness between multi-word terms, long pre-processing times
• NGD (Normalized Google distance) (+) large vocab, because it uses any search engine (like Google); (−) can measure relatedness between whole sentences or documents but the larger the sentence or document the more ingenuity is required, Cilibrasi & Vitanyi (2007), reference below.^[48]
• NCD (Normalized Compression Distance)
• ESA (Explicit Semantic Analysis) based on Wikipedia and the ODP
• SSA (Salient Semantic Analysis) which indexes terms using salient concepts found in their immediate context.
• n° of Wikipedia (noW), inspired by the game Six Degrees of Wikipedia, is a distance metric based on the hierarchical structure of Wikipedia. A directed-acyclic graph is first constructed and later, Dijkstra's shortest path algorithm is employed to determine the noW value between two terms as the geodesic distance between the corresponding topics (i.e. nodes) in the graph.
• VGEM (Vector Generation of an Explicitly-defined Multidimensional Semantic Space) (+) incremental vocab, can compare multi-word terms (−) performance depends on choosing specific dimensions
• BLOSSOM (Best path Length On a Semantic Self-Organizing Map) (+) uses a Self Organizing Map to reduce high-dimensional spaces, can use different vector representations (VGEM or word-document matrix), provides 'concept path linking' from one word to another (−) highly experimental, requires nontrivial SOM calculation
• SimRank
• NASARI:^[49] Sparse vector representations constructed by applying the hypergeometric distribution over the Wikipedia corpus in combination with BabelNet taxonomy. Cross-lingual similarity is currently also possible thanks to the multilingual and unified extension.^[50]

Semantics-based similarity

• Good Common Subsumer-(GCS)-based Semantic Similarity Measure ^[51]
• Comment on application of semantics-based similarity to biomedical ontologies ^[52]

Gold standards

Researchers have collected datasets with similarity judgements on pairs of words, which are used to evaluate the cognitive plausibility of computational measures. For a list of datasets, see this compiled list of Word Similarity Datasets.

See also

• Terminology extraction
• Coherence (linguistics)
• Analogy
• Semantic differential
• Semantic similarity network
• Word2Vec

References

1. Harispe S., Ranwez S. Janaqi S., Montmain J. (2015). "Semantic Similarity from Natural Language and Ontology Analysis". Synthesis Lectures on Human Language Technologies 8:1: 1–254. doi:10.2200/S00639ED1V01Y201504HLT027.
2. A. Ballatore, M. Bertolotto, D.C. Wilson (2014). "An evaluative baseline for geo-semantic relatedness and similarity". GeoInformatica 18:4: 747–767.
3. Budanitsky, Alexander; Hirst, Graeme (2001). "Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures" (PDF). Workshop on WordNet and Other Lexical Resources, Second meeting of the North American Chapter of the Association for Computational Linguistics (Pittsburgh).
4. Pesquita, Catia; Faria, Daniel; Falcão, André O.; Lord, Phillip; Couto, Francisco M. (2009). Bourne, Philip E., ed. "Semantic Similarity in Biomedical Ontologies". PLoS Computational Biology 5 (7): e1000443. doi:10.1371/journal.pcbi.1000443. PMC 2712090. PMID 19649320.
5. Guzzi, Pietro Hiram; Mina, Marco; Cannataro, Mario; Guerra, Concettina (2012). "Semantic similarity analysis of protein data: assessment with biological features and issues". Briefings in Bioinformatics 13 (5): 569–585. doi:10.1093/bib/bbr066. PMID 22138322.
6. Benabderrahmane, Sidahmed; Smail Tabbone, Malika; Poch, Olivier; Napoli, Amedeo; Devignes, Marie-Domonique. (2010). "IntelliGO: a new vector-based semantic similarity measure including annotation origin". Biomed Central 11: 588. doi:10.1186/1471-2105-11-588. PMC 3098105. PMID 21122125.
7. Couto, F., Silva, M., & Coutinho, P. (2003). Implementation of a functional semantic similarity measure between gene-products. DI/FCUL TR 03–29, University of Lisbon
8. Pesquita, C., Faria, D., Falcão, A., Lord, P., & Couto, F. (2009). Semantic similarity in biomedical ontologies. PLoS Computational Biology, 5:e1000443
9. Couto, F., Silva, M., & Coutinho, P. (2005). "Semantic similarity over the gene ontology: Family correlation and selecting disjunctive ancestors". Proc. of the ACM Conference in Information and Knowledge Management (CIKM): 343. doi:10.1145/1099554.1099658. ISBN 1595931406.
10. Couto, F., Silva, M., & Coutinho, P. (2007). "Measuring semantic similarity between Gene Ontology terms". Data and Knowledge Engineering 61: 137–152. doi:10.1016/j.datak.2006.05.003.
11. Ferreira, João D.; Couto, Francisco M. (2010). Mitchell, John B. O., ed. "Semantic Similarity for Automatic Classification of Chemical Compounds". PLoS Computational Biology 6 (9): e1000937. doi:10.1371/journal.pcbi.1000937. PMC 2944781. PMID 20885779.
12. Ferreira, João D.; Couto, Francisco M. (2011). "Generic semantic relatedness measure for biomedical ontologies" (PDF). ICBO 2011 Proceedings.
13. Köhler, S; Schulz, MH; Krawitz, P; Bauer, S; Dolken, S; Ott, CE; Mundlos, C; Horn, D; et al. (2009). "Clinical diagnostics in human genetics with semantic similarity searches in ontologies". American Journal of Human Genetics 85 (4): 457–64. doi:10.1016/j.ajhg.2009.09.003. PMC 2756558. PMID 19800049.
14. "ProteInOn".
15. "CMPSim".
16. "CESSM".
17. Janowicz, K., Raubal, M. and Kuhn, W. (2011). "The semantics of similarity in geographic information retrieval". Journal of Spatial Information Science 2: 29–57. doi:10.5311/josis.2011.2.3.
18. "SIM-DL similarity server". CiteSeerX: 10.1.1.172.5544.
19. "Geo-Net-PT Similarity Calculator".
20. "Geo-Net-PT".
21. A. Ballatore, D.C. Wilson, M. Bertolotto. "Geographic Knowledge Extraction and Semantic Similarity in OpenStreetMap". Knowledge and Information Systems: 61–81.
22. Kaur, I. & Hornof, A.J. (2005). "A Comparison of LSA, WordNet and PMI for Predicting User Click Behavior". Proceedings of the Conference on Human Factors in Computing, CHI 2005: 51–60. doi:10.1145/1054972.1054980. ISBN 1581139985.
23. Similarity-based Learning Methods for the Semantic Web (C. d'Amato, PhD Thesis)
24. Gracia, J. and Mena, E. (2008). "Web-Based Measure of Semantic Relatedness" (PDF). Proceedings of the 9th international conference on Web Information Systems Engineering (WISE '08) (Springer-Verlag, Berlin, Heidelberg): 136–150.
25. Raveendranathan, P. (2005). Identifying Sets of Related Words from the World Wide Web. Master of Science Thesis, University of Minnesota Duluth.
26. Wubben, S. (2008). Using free link structure to calculate semantic relatedness. In ILK Research Group Technical Report Series, nr. 08-01, 2008.
27. Juvina, I., van Oostendorp, H., Karbor, P., & Pauw, B. (2005). Towards modeling contextual information in web navigation. In B. G. Bara & L. Barsalou & M. Bucciarelli (Eds.), 27th Annual Meeting of the Cognitive Science Society, CogSci2005 (pp. 1078–1083). Austin, Tx: The Cognitive Science Society, Inc.
28. Navigli, R., Lapata, M. (2007). Graph Connectivity Measures for Unsupervised Word Sense Disambiguation, Proc. of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India, January 6-12th, 2007, pp. 1683–1688.
29. Pirolli, P. (2005). "Rational analyses of information foraging on the Web". Cognitive Science 29 (3): 343–373. doi:10.1207/s15516709cog0000_20. PMID 21702778.
30. Pirolli, P., & Fu, W.-T. (2003). "SNIF-ACT: A model of information foraging on the World Wide Web". Lecture Notes in Computer Science 2702. pp. 45–54. doi:10.1007/3-540-44963-9_8.
31. Turney, P. (2001). Mining the Web for Synonyms: PMI versus LSA on TOEFL. In L. De Raedt & P. Flach (Eds.), Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001) (pp. 491–502). Freiburg, Germany.
32. Pekar, Viktor; Staab, Steffen (2002). "Proceedings of the 19th international conference on Computational linguistics -" 1: 1. doi:10.3115/1072228.1072318. |chapter= ignored (help)
33. Cheng, J; Cline, M; Martin, J; Finkelstein, D; Awad, T; Kulp, D; Siani-Rose, MA (2004). "A knowledge-based clustering algorithm driven by Gene Ontology". Journal of biopharmaceutical statistics 14 (3): 687–700. doi:10.1081/BIP-200025659. PMID 15468759.
34. Wu, H; Su, Z; Mao, F; Olman, V; Xu, Y (2005). "Prediction of functional modules based on comparative genome analysis and Gene Ontology application". Nucleic Acids Research 33 (9): 2822–37. doi:10.1093/nar/gki573. PMC 1130488. PMID 15901854.
35. Del Pozo, Angela; Pazos, Florencio; Valencia, Alfonso (2008). "Defining functional distances over Gene Ontology". BMC Bioinformatics 9: 50. doi:10.1186/1471-2105-9-50. PMC 2375122. PMID 18221506.
36. Philip Resnik (1995). Chris S. Mellish (Ed.), ed. "Using information content to evaluate semantic similarity in a taxonomy". Proceedings of the 14th international joint conference on Artificial intelligence (IJCAI'95) (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA) 1: 448–453.
37. Dekang Lin. 1998. An Information-Theoretic Definition of Similarity. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML '98), Jude W. Shavlik (Ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 296-304
38. Ana Gabriela Maguitman, Filippo Menczer, Heather Roinestad, Alessandro Vespignani: Algorithmic detection of semantic similarity. WWW 2005: 107-116
39. J. J. Jiang and D. W. Conrath. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In International Conference on Research on Computational Linguistics (ROCLING X), pages 9008+, September 1997
40. Couto, F. & Silva, M. (2011), Disjunctive Shared Information between Ontology Concepts: application to Gene Ontology. Journal of Biomedical Semantics, 2:5
41. Couto, F., Silva, M., & Coutinho, P. (2007). Measuring semantic similarity between Gene Ontology terms. Data and Knowledge Engineering, 61:137–152
42. M. T. Pilehvar, D. Jurgens and R. Navigli. Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity.. Proc. of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Sofia, Bulgaria, August 4–9, 2013, pp. 1341-1351.
43. Dong, Hai (2009). "A Hybrid Concept Similarity Measure Model for Ontology Environment". Lecture Notes in Computer Science 5872: 848-857.
44. Dong, Hai (2011). "A context-aware semantic similarity model for ontology environments". Concurrency and Computation: Practice and Experience 23 (2): 505-524.
45. Catia Pesquita, Daniel Faria, Hugo Bastos, António Ferreira, Andre O Falcao, Francisco Couto 2008: Metrics for GO based protein semantic similarity: a systematic evaluation. BMC Bioinformatics Suppl 5(9), S4
46. Landauer, T. K.; Dumais, S. T. (1997). "A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge". Psychological Review 104 (2): 211–240. doi:10.1037/0033-295x.104.2.211.
47. Landauer, T. K., Foltz, P. W., & Laham, D. (1998). "Introduction to Latent Semantic Analysis" (PDF). Discourse Processes 25: 259–284. doi:10.1080/01638539809545028.
48. "Google Similarity Distance".
49. J. Camacho-Collados, M. T. Pilehvar, and R. Navigli. NASARI: a Novel Approach to a Semantically-Aware Representation of Items. In Proceedings of the North American Chapter of the Association of Computational Linguistics (NAACL 2015), Denver, USA, pp. 567-577, 2015
50. J. Camacho-Collados, M. T. Pilehvar, and R. Navigli. A Unified Multilingual Semantic Representation of Concepts. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), Beijing, China, July 27–29, pp. 741-751, 2015
51. C. d'Amato, S. Staab, and N. Fanizzi. On the influence of description logics ontologies on conceptual similarity. Knowledge Engineering: Practice and Patterns, pages 48-63, 2008 doi:10.1007/978-3-540-87696-0_7
52. F. Couto and H. Pinto, The next generation of similarity measures that fully explore the semantics in biomedical ontologies, Journal of Bioinformatics and Computational Biology, vol. in press, 2013. preprint

• Cilibrasi, R.L. & Vitanyi, P.M.B. (2007). "The Google Similarity Distance". IEEE Trans. Knowledge and Data Engineering 19 (3): 370–383. doi:10.1109/TKDE.2007.48.
• Dumais, S (2003). "Data-driven approaches to information access". Cognitive Science 27 (3): 491–524. doi:10.1207/s15516709cog2703_7.
• Ferreira, J. & Couto, F. (2010). Semantic similarity for automatic classification of chemical compounds. PLoS Computational Biolology 6(9): e1000937, 2010.
• Gabrilovich, E. and Markovitch, S. (2007). Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis, Proceedings of The 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007.
• Lee, M. D., Pincombe, B., & Welsh, M. (2005). An empirical evaluation of models of text document similarity. In B. G. Bara & L. Barsalou & M. Bucciarelli (Eds.), 27th Annual Meeting of the Cognitive Science Society, CogSci2005 (pp. 1254–1259). Austin, Tx: The Cognitive Science Society, Inc.
• Lemaire, B., & Denhiére, G. (2004). Incremental construction of an associative network from a corpus. In K. D. Forbus & D. Gentner & T. Regier (Eds.), 26th Annual Meeting of the Cognitive Science Society, CogSci2004. Hillsdale, NJ: Lawrence Erlbaum Publisher.
• Lindsey, R., Veksler, V.D., Grintsvayg, A., Gray, W.D. (2007). "The Effects of Corpus Selection on Measuring Semantic Relatedness" (PDF). Proceedings of the 8th International Conference on Cognitive Modeling, Ann Arbor, MI.
• Navigli, R., Lapata, M. (2010). "An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation". IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 32(4), IEEE Press, 2010, pp. 678–692.
• Veksler, V.D. & Gray, W.D. (2006). "Test Case Selection for Evaluating Measures of Semantic Distance" (PDF). Proceedings of the 28th Annual Meeting of the Cognitive Science Society, CogSci2006.
• Wong, W., Liu, W. & Bennamoun, M. (2008) Featureless Data Clustering. In: M. Song and Y. Wu; Handbook of Research on Text and Web Mining Technologies; IGI Global. [ISBN 978-1-59904-990-8] (the use of NGD and noW for term and URI clustering)

External links

• List of related literature
• WordNet::Similarity (using WordNet as an ontology)
• WordNet Explorer (interactive graphic WordNet database editor)
• Survey articles:
  • Semantic Measures, i.e., semantic similarity, distance, relatedness... (Harispe et al. 2015)
  • Semantic Similarity Measures (C. d'Amato, S. Staab, N. Fanizzi, EKAW 2008, Springer-Verlag)
• Algorithm, Implementation and Application of the SIM-DL Similarity Server (Introduction to the SIM-DL Similarity Server)

Software

• Semantic Measures Library (SML), a highly tunable and open source JAVA library dedicated to the computation and analysis of semantic measures. The library is generic as it can be used on multiple ontologies/terminologies e.g. Gene Ontology, Medical Subject Headings, SNOMED CT, WordNet, or semantic graphs expressed in RDF Schema, Web Ontology Language, Open Biomedical Ontologies languages. The core developers also maintain the SML-Toolkit, a set of command line tools giving non-developers access to SML functionalities e.g. to perform large scale computation of semantic measures. Tutorials, downloads and documentation for both the SML and the SML-Toolkit are available at http://www.semantic-measures-library.org/.
• WordNet-Similarity, an open source package for computing the similarity and relatedness of concepts found in WordNet
• UMLS-Similarity, an open source package for computing the similarity and relatedness of concepts found in the Unified Medical Language System (UMLS)
• SEMILAR - A Semantic Similarity Toolkit, java based library, tool, and data related to measuring similarity and relatedness of text in different granularity (all free for research purposes).^[1]
• DISCO Builder and DISCO API, a Java toolkit to compute the similarity between words and phrases. Allows to import word spaces generated with Word2vec. Freely available for research, API is Apache licensed.
• REST API for computing Cosine, Jaccard and Dice based similarity.
• TakeLab STS system - One of the top performing short text semantic similarity systems from Semeval 2012

Web services

• ESA Semantic Relatedness, a web API to compute semantic relatedness between pairs of words or text excerpts
• Serelex Semantic Relatedness, a web service that finds semantically related words based on the Serelex semantic similarity measure, which relies on a text corpus and a set of lexico-syntactic patterns. Description of this service is available at Panchenko et al. (2013)
• Cosine Similarity computing service, an online service that computes cosine text similarity between two documents
• WordNet-Similarity, a web interface to WordNet-Similarity
• UMLS-Similarity, a web interface to UMLS-Similarity
• Semantic Link, finds related words using Wikipedia-based mutual information (MI)
• UMBC SimService, a web interface to top N similar words and phrase similarity
• REST API - for computing Cosine, Jaccard and Dice based similarity.

1. Rus, V., Lintean, M. C., Banjade, R., Niraula, N. B., & Stefanescu, D. (2013, August). SEMILAR: The Semantic Similarity Toolkit. In ACL (Conference System Demonstrations) (pp. 163-168).