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Semantic decomposition (natural language processing)

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A semantic decomposition is an algorithm that breaks down the meanings of phrases or concepts into less complex concepts.[1] The result of a semantic decomposition is a representation of meaning. This representation of meaning can be used for various tasks, particularly those related to artificial intelligence or machine learning. Semantic decompositions are particularly common in natural language processing applications.

The basic idea of a semantic decomposition is taken from the learning skills of adult humans, where words are explained using other words, which is based on the Meaning-text theory. The Meaning-text theory is used as a theoretical linguistic framework, to describe the meaning of concepts with other concepts.

Background

An artificial notion of meaning needs to be created for a strong AI to emerge.[2]. Without a language, and with that the meaning of the words used in this language, an AI is unable to think. Without thought, there is only reacting, no reasoning. AI today is able to syntactically capture language for many specific problems but never establishes meaning for the words of these languages or is able to abstract to concepts [3].

Creating an artificial representation of meaning requires the analysis of what meaning is. There are many terms associated with meaning, like semantics, pragmatics, knowledge, understanding or word sense [4]. All of those describe an aspect and bear a multitude of theories explaining what meaning is. These theories need to be analysed to develop an artificial notion of meaning best fitted to our current state of knowledge.

Graph representations

Abstract approach on how knowledge representation and reasoning allow a problem specific solution (answer) to a given problem (questions)

Representing meaning as a graph is one of the two ways AI, cognition and linguistic research think about meaning (connectionist view). Logicians and formal representation of meaning on the other side build upon the idea of symbolic representation where description logics describe languages and the meaning of symbols. This neat vs. scruffy discussion is going on for the last 40 years [5]. The research so far has identified semantic measures and with that Word Sense Disambiguation Word-sense disambiguation (WSD) - the differentiation of meaning of words - as the main problem of language understanding[6]. As an AI-complete problem WSD is a core problem of natural language understanding [7] [8]. AI approaches which use knowledge given reasoning creates a notion of meaning combining the state of the art knowledge of natural meaning with the symbolic and connectionist formalisation of meaning for AI. The abstract approach is shown in Figure. First, we create a connectionist knowledge representation as a semantic network consisting of concepts and their relations which will serve as the basis for the representation of meaning [9] [10] [11] [12].

This graph is built out of different knowledge sources like WordNet, Wiktionary and BabelNET. Here the graph is created by lexical decomposition which breaks each concept semantically down until a set so semantic primes are reached [1]. The primes are taken from the theory of the Natural Semantic Metalanguage [13], which we have analysed for their usefulness in formal languages[14]. Upon this graph marker passing [15] [16] [17] is used to create the dynamic part of meaning representing thoughts[18]. The marker passing algorithm, where symbolic information is passed along relations form one concept to another, uses node and edge interpretation to guide its markers. The node and edge interpretation model is the symbolic influence of certain concepts.

Future work this areas of research will use the created representation of meaning to build heuristics and evaluate them through capability matching and agent planning, chatbots or other applications of natural language understanding.

See also

References

  1. ^ a b Nick Riemer. The Routledge Handbook of Semantics. Routledge, August 2015
  2. ^ Loizos Michael. Jumping to Conclusions. In International Workshop on Defeasible and Ampliative Reasoning, Buenos Aires, 2015
  3. ^ John F Sowa. Knowledge Representation: Logical, Philosophical, and Computational Foundations. Thomson Learning, 2000
  4. ^ Sebastian Löbner Semanitk eine Einführung, 2003
  5. ^ Marvin L Minsky. Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI Magazine, 12(2):34, 1991
  6. ^ ] Edmonds Philip Agirre, Eneko. Word sense disambiguation: Algorithms and applications, volume 33. Springer, 2007
  7. ^ Nancy Ide and Jean Veronis. Introduction to the special issue on word sense disambiguation: the state of the art. Computational Linguistics, 24(1):2-40, 1998
  8. ^ Roman V Yampolskiy. AI-Complete, AI-Hard, or AI-Easy - Classification of Problems in AI. MAICS, pages 94-101, 2012
  9. ^ Katia Sycara, Matthias Klusch, Seth Widoff, and Jianguo Lu. Dynamic service matchmaking among agents in open information environments. SIG- MOD Record, 28(1):47-53, 1999
  10. ^ Phillipa Oaks, Arthur H M ter Hofstede, and David Edmond. Capabilities: Describing What Ser- vices Can Do. In Service-Oriented Computing - ICSOC 2003, pages 1-16 pringer Berlin Heidelberg, 2003
  11. ^ Johannes Fähndrich est First Search Planning of Service Composition Using Incrementally Redefined Context-Dependent Heuristics. In the German Conference Multiagent System Technologies, pages 404-407, Springer Berlin Heidelberg, 2013
  12. ^ Johannes Fähndrich, Sebastian Ahrndt, and Sahin Albayrak. Towards Self-Explaining Agents. PAAMS (), 221(Chapter 18):147-154, 2013
  13. ^ Cliff Goddard and Anna Wierzbicka. Semantic and Lexical Universals. Theory and Empirical Findings. John Benjamins Publishing, 1994
  14. ^ Johannes Fähndrich, Sebastian Ahrndt, and Sahin Albayrak. Formal Language Decomposition into Semantic Primes. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 3(8):56, 2014
  15. ^ James A Hendler. Integrating marker- passing and problem-solving: A spreading activation approach to improved choice in planning. Hillsdale, N.J. : Lawrence Erlbaum Associates, 1988
  16. ^ Graeme Hirst. Semantic Interpretation and the Resolution of Ambiguity. Cambridge University Press, March 1992
  17. ^ Johannes Fähndrich, Sebastian Ahrndt, and Sahin Albayrak. Self-Explanation through Semantic Annotation and (automated) Ontology Creation: A Survey. In 10th International Symposium Advances in Artificial Intelligence and Applications, pages 1-15, ACM 2015
  18. ^ F Crestani. Application of Spreading Activation Techniques in Information Retrieval. Artificial Intelligence Review, 11(6):453-482, 1997
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