Lexical Semantics for the Semantic Web

1. Lexical Semantics for the Semantic Web Patrick Hanks Masaryk University, Brno Czech Republic hanks@fi,muni.cz UFAL, Mathematics Faculty, Charles University in Prague 1

2. Outline of the talk • A neglected aspect of Tim Berners-Lee’s vision: – Introducing semantics to the semantic web – Computing meaning and inferences in free text • Patterns in text and how to use them • Building a resource that encodes patterns – linking meanings (implicatures) to patterns (not to words) – A “pattern dictionary” – What does the pattern dictionary look like? – The role of an ontology in a pattern dictionary 2

3. Aims of the Semantic Web • “To enable computers to manipulate data meaningfully” • “Most of the Web's content today is designed for humans to read, not for computer programs to manipulate meaningfully.” — Berners-Lee et al., Scientific American, 2001 3

4. A neglected aspect of Berners-Lee’s vision • “Web technology must not discriminate between the scribbled draft and the polished performance.” —T. Berners-Lee et al., Scientific American, 2001 • The vision includes being able to process the meaning and implicatures of free text • not just pre-processed tagged texts – Wikis, names, addresses, appointments, and suchlike. 4

5. A paradox • “Traditional KR systems typically have been centralized, requiring everyone to share exactly the same definition of common concepts such as 'parent' or 'vehicle'.” – Berners-Lee et al. 2001. – Implying that SW is more tolerant? – Apparently not: “Human languages thrive when using the same term to mean somewhat different things, but automation does not.” - -Ibid. • 5

6. The root of the problem Scientists from Leibniz to the present have wanted word meaning to be precise and certain. But it isn’t. Meaning in natural language is vague and probabilistic Some theoretical linguists (and CL researchers), not liking fuzziness in data, have preferred to disregard data in order to preserve theory Do not allow SW research to fall into this trap To fulfil Berners-Lee’s dream, we need to be able to compute the meaning of un-pre-processed documents • – • 6

7. What NOT to do for the SW • The meaning of the English noun second is vague: “a short unit of time” or “1/60 of a minute”. – Wait a second. – He looked at her for a second. • It is also a very precisely defined technical term in certain scientific contexts – the basic SI unit of time: – “the duration of 9,192,631,770 cycles of radiation corresponding to the transition between two hyperfine levels of the ground state of an atom of caesium 133.” • If we try to stipulate a precise meaning for all terms in advance of using them, we’ll never be able to fulfil the dream – and we will invent an unusable language 7

8. Precision and vagueness • Stipulating a precise definition for an ordinary word such as second removes it from ordinary language. • When it is given a precise, stipulative definition, an ordinary word becomes a technical term. • “An adequate definition of a vague concept must aim not at precision but at vagueness; it must aim at precisely that level of vagueness which characterizes the concept itself.” – Wierzbicka 1985, pp.12-13 8

9. The paradox of natural language • Word meaning may be vague and fuzzy, but people use words to make very precise statements • This can be done because text meaning is holistic, e.g. – “fire” in isolation is very ambiguous; – But “He fired the bullet that was recovered from the girl's body” is not at all ambiguous – “Ithaca” is ambiguous; – But “Ithaca, NY” is much less ambiguous. Even the tiniest bit of (relevant) context helps. 9

10. What is to be done? • Process only the (strictly defined) mark-up of documents, not their linguistic content? – And so abandon the dream of enabling computers to manipulate linguistic content? • Force humans to conform to formal requirements when writing documents? – Not a serious practical possibility • Teach computers to deal with natural language in all its fearful fuzziness? – Maybe this is what we need to do 10

11. Hypertext and relevance • “The power of hypertext is that anything can link to anything.” – Berners-Lee et al., 2001 • Yes, but we need procedures for determining (automatically) what counts as a relevant link, e.g. – Firing a person is relevant to employment law. – Firing a gun is relevant to warfare and armed robbery. 11

12. How do we know who is doing what to whom? Through context (a standard, uncontroversial answer) • • But teasing out relevant context is tricky: – Firing a person: [[Person]] MUST be mentioned – Whereas firing a gun occurs in patterns where neither [[Firearm]] nor [[Projectile]] are mentioned, e.g. – The police fired into the crowd/over their heads/wide. • Negative evidence can be important: – “He fired” cannot mean he dismissed someone from employment • Relevant context is cumulative – So correlations among arguments are often needed 12

13. How to compute meaning for the Semantic Web STEP 1. Identify all the normal patterns of normal utterances by data analysis STEP 2. Develop a resource that says precisely what the basic implicatures of each pattern are, e.g. [[Human]] fire [Adv[Direction]] = [[Human]] causes [[Firearm]] to discharge [[Projectile]] STEP 3. Populate the semantic types in an ontology STEP 4. Develop a linguistic theory that distinguishes norms from exploitations Abandon the received theories of speculative linguists STEP 5. Develop procedures for finding best matches between a free text statement and a pattern. 13

14. The double helix of language: norms and exploitations • A natural language consists of TWO kinds of rule- governed behaviour: – Using words normally – Exploiting the norms • We don’t even know what the norms of any language are, still less the exploitation rules • People have assumed that norms of usage are obvious – But only some of the things that are obvious are true – We need to identify the norms by painstaking empirical analysis of evidence • There is not a sharp dividing line between ‘norm’ and ‘exploitation’ • Today’s norm is tomorrow’s exploitation 14

15. Corpus Pattern Analysis (CPA) 1. Identifies normal usage patterns for each word – Each pattern include a verb, its valencies, and the semantic type(s) of each argument (valence) Associates a meaning (“implicature”) with each pattern (NOT with each word) Provides a basis for matching occurrences of target words in unseen texts to their nearest pattern (“norm”) CPA is the basis for a “Pattern Dictionary” (demo): – http://nlp.fi.muni.cz/projekty/cpa/ – Click on “web access’ in line 1 2. 3. 4. 15

16. Focusing arguments by semantic- type alternation • You can calm a person, calm a horse, calm someone’s nerves, fears, or anxiety. – These all activate the same meaning of the verb calm. Anxiety does not have the required semantic type (anxiety is not [[Animate]]) – However, the expected animate argument is present – but only as a possessive. And even if there is no possessive, being an attribute of [[Animate]] is part of the meaning of nerves, fear, anxiety, etc. • Regular alternations such as these have a focusing function. They do not activate different senses. • Other examples: – Repair a car, repair the engine (of a car), repair the damage – Treat a person, treat her injuries, treat her injured arm 16

17. Ontologies • The arguments of CPA patterns are expressed as semantic types, related to a shallow semantic ontology. • The term ontology is – has become – highly ambiguous: • SW ontologies are, typically, interlinked networks of things like address lists, dates, events, and websites, with html mark-up showing attributes and values • They differ from philosophical ontologies, which are theories about the nature of all the things in the universe that exist • They also differ from lexical ontologies such as WordNet, which are networks of words with supposed conceptual relations • The CPA shallow ontology is a device for grouping semantically similar words together to facilitate meaning processing 17

18. The CPA Shallow Ontology • The CPA Shallow Ontology is a “bag of bags of words” • Developed, bottom-up, by cluster analysis of corpora: – The nouns that NORMALLY occur in the same syntagmatic slot in relation to a given verb are grouped into a cluster – A cluster of different nouns activate the same meaning of the verb – The cluster is named with a semantic type, e.g. [[Human]], [[Event]], [[Abstract]], [[Artefact]], etc. – Each cluster is compared with similar clusters occurring with other verbs. Each combination of clusters constitutes a lexical set. – Identically named clusters contain slightly different members (lexical items) – Therefore, lexical sets “shimmer”. 18

19. The Predictive Power of Lexical Sets • EXAMPLE: A noun, ‘meeting’ has been classified with semantic type [[Event]] at both arrange and attend Suppose ‘meeting’ is found in the direct object slot after leave or run—but not frequently enough to have been included in a cluster for those verbs in the Ontology • However, the patterns “[[Human]] leave [[Event]]” and “[[Human]] run [[Event]]” will be found in the Pattern Dictionary – Then there is a high probability that ‘meeting’ belongs there (even though not listed as typical), activating probable implicatures: – leave = "go away from” – run = "organize and cause to function efficiently” • 19

20. Phraseology in Computational Linguistics • Computational linguists are turning away from word-by- word analysis (the “Lego bricks” method, inherited from Frege) to phraseological analysis. E.g. – Marine Carpuat and Dekai Wu. 2007. How phrase sense disambiguation outperforms word sense disambiguation for statistical machine translation. In Proceedings, Conference on Theoretical and Methodological Issues in Machine Translation (TMI 2007). Skovde, Sweden • The Pattern Dictionary provides an inventory of patterns – A benchmark for NLP researchers using patterns – A benchmark for introducing semantics to the Semantic Web 20

21. The English Pattern Dictionary: current status • Focuses on verbs – Specifically, the correlations among the lexical and semantic values of the arguments of each sense of each verb • 700 verbs analysed so far – 400 verbs complete, finalized, checked and released – 300 more are work in progress, awaiting checking – There are approximately 6000 verbs in English, so we have done about 10% • Shallow ontology in development • New lexically driven theory of language, which is precise about the vague phenomenon of language – Hanks (forthcoming): Analysing the Lexicon: Norms and Exploitations. MIT Press 21

22. The English Pattern Dictionary: the future • 5,400 more verbs to analyse (then the adjectives) • Develop a different procedure for nouns (noun-y nouns) • Finalize the CPA shallow ontology and populate it • Pattern dictionaries for other languages – Czech – German (A. Geyken, Berlin) – Italian (E. Jezek, U. of Pavia) • Theoretical work: – Typology of exploitations – Implications of CPA for parsing theory – Alternation of semantic types in arguments – Relationship between semantic types and semantic roles – Links between the Pattern Dictionary and FrameNet – 22

23. Conclusions To enable computers to “manipulate data meaningfully” (the raw data itself, not just tags added to the data), we need: • an inventory of patterns of normal usage for each word – a “pattern dictionary” • a theory that distinguishes normal usage from exploitations of norms for rhetorical, poetic, and other purposes • pattern-matching procedures: text < — > pattern dictionary • a statistical, probabilistic approach to identifying meaning. – Only then will computers be able to compute the “meaning” of texts, understand the implicatures, translate them, retrieve data from them, and manipulate them in other ways – At that point, we shall be a little closer to realizing Berners-Lee’s 2001 dream 23