1 Computing Real Language Meaning for the Semantic Web Patrick Hanks Masaryk University, Brno Czech Republic UFAL, Mathematics Faculty, Charles University in Prague

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? Future work: prospects and challenges 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

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 –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.

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

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

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

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.

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

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.

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

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

The double helix: 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 a 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 the norms by painstaking empirical analysis of evidence 14

15 Corpus Pattern Analysis (CPA) 1.Identifies normal usage patterns for each word –Patterns include semantic types and lexical sets of arguments (valencies) 2.Associates a meaning (“implicature”) with each pattern (NOT with each word) 3.Provides a basis for matching occurrences of target words in unseen texts to their nearest pattern (“norm”)

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

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 600 verbs analysed so far –200 verbs complete, finalized, checked and released –400 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 17

18 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 to facilitate meaning processing

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 – 19

20 Conclusions Word meaning is vague, but the vagueness can be captured and measured, using corpus evidence In context, word meaning often becomes precise –But it can also be creative We must distinguish precision from creativity To do reliable inferencing on ordinary language texts, we need to compare actual usage with patterned norms, and chose the best match (and compute how good the match is)