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Language Technology (A Machine Learning Approach) Antal van den Bosch & Walter Daelemans [email_address] [email_address] http://ilk.uvt.nl/~antalb/ltua

Programme 24/2 [Antal] Intro ML for NLP & WEKA / Decision Trees 3/3 [Walter] ML for shallow parsing 10/3 [Antal] ML for morphology and phonology 17/3 [Antal] ML for Information extraction 24/3 [Antal] ML for discourse 31/3 [V éronique ] ML for coreference 21/4 [Antal] Memory & Representation 28/4 [Antal] Modularity / More Data 5/5 [Walter] ML for document classification

Evaluation Assignments with different modules (fixed deadline) Final assignment

Text Meaning LT Components Applications Lexical / Morphological Analysis Syntactic Analysis Semantic Analysis Discourse Analysis Tagging Chunking Word Sense Disambiguation Grammatical Relation Finding Named Entity Recognition Reference Resolution OCR Spelling Error Correction Grammar Checking Information retrieval Information Extraction Summarization Machine Translation Document Classification Ontology Extraction and Refinement Question Answering Dialogue Systems

Text Representation Units Character n-grams Words, phrases, heads of phrases POS tags Parse tree (fragment)s Grammatical Relations Frames and scripts “meaning” (?)

Text is a special kind of data Direct entry, OCR (.99 accuracy), Speech Recognition output (.50-.90 accuracy), … What we have: Characters, character n-grams, words, word n-grams, lay-out, counts, lengths, … What we want: Meaning (answering questions, relating with previous knowledge) Bridging the gap: Tagging, lemmatization, phrase chunking, grammatical relations, … I.e.: Language Technology

Meaning Speech Text Speech recognition Language comprehension Text generation Speech synthesis translation

Language Technology (Natural Language Processing, Computational Linguistics) is based on the complex transformation of linguistic representations Examples from text to speech from words to morphemes from words to syntactic structures from syntactic structures to conceptual dependency networks

In this transformation, two processes play a role segmentation of representations disambiguation of possible transformations of representation units Similar representations at input level correspond to similar representations at the output level Complexity because of context-sensitivity (regularities, subregularities, exceptions)

gebruiksvriendelijkheid ge+bruik+s+vriend+elijk+heid Systran: Fremdzugehen -> External train marriages The old man the boats det N-plur V-plur det N-plur Punc The old man the boats (S (NP (DET the) (N old)) (VP (V man) (NP (DET the) (N boats)))) Systran: De oude man de boten Systran: De prins bespreekt (zijn huwelijk) (met Verhofstadt) The prince discusses (its marriage to Verhofstadt) (S (NP (DET the) (N old)) (VP (V man) (NP (DET the) (N boats)))) (man-action (agent (def plur old-person)) (object (def plur boat)))

How to reach Language Understanding ? A fundamental solution for the problem of language understanding presupposes Representation and use of knowledge / meaning Acquisition of human-level knowledge

What is meaning ? Eleni eats a pizza with banana Pizza = {p1, p2, p3, …} Eat ={<Nicolas,p1>,<Nicolas,p3>,<Eleni,p2>,…} Contain ={<p1,ansjovis>,<p1,tomaat>,<p2,banaan>,…} x=p2 Semantic networks, Frames First-order predicate calculus Set theory Eleni banana pizza eat contain  (x): pizza(x)  eat (Eleni,x)  contain (x, banaan ) “ Symbol grounding” problem Representation and processing of time, causality, modality, defaults, common sense, … “ Meaning is in the mind of the beholder”

Language Technology Language Data Model Input Representation Output Representation Acquisition Processing

Deductive Route Language Data Model Input Representation Output Representation S -> NP VP NP -> ART A N NP -> ART N VP -> V NP ...

Deductive Route Acquisition Construct a (rule-based) model about the domain of the transformation. Processing Use rule-based reasoning, deduction, on these models to solve new problems in the domain.

Inductive Route Language Data Model Input Representation Output Representation De computertaalkunde, in navolging van de taalkunde aanvankelijk sterk regel-gebaseerd, is onder druk van toepasbaarheid en grotere rekenkracht de laatste tien jaar geleidelijk geevolueerd naar een meer statistische, corpus-gebaseerde en inductieve benadering. De laatste jaren hebben ook technieken uit de theorie van zelflerende systemen (Machine Learning, zie Mitchell, 1998 voor een inleiding) aan belang gewonnen. Deze technieken zijn in zekere zin p(corpustaalkunde|de), p(in|corpustaalkunde), p(corpustaalkunde), p(de), p(in), ...

Inductive Route Acquisition Induce a stochastic model from a corpus of “examples” of the transformation. Processing Use statistical inference (generalization) from the stochastic model to solve new problems in the domain.

Advantages Deductive Route Linguistic knowledge and intuition can be used Precision Inductive Route Fast development of model Good coverage Good robustness (preference statistics) Knowledge-poor Scalable / Applicable

Problems Deductive Route Representation of sub/irregularity Cost and time of model development (Not scalable / applicable) Inductive Route Sparse data Estimation of relevance statistical events

Text Mining Automatic extraction of reusable information (knowledge) from text, based on linguistic features of the text Goals: Data mining (KDD) from unstructured and semi-structured data (Corporate) Knowledge Management “Intelligence” Examples: Email routing and filtering Finding protein interactions in biomedical text Matching resumes and vacancies

Document Set of Documents Author Recognition Document Dating Language Identification Text Categorization Information Extraction Summarization Question Answering Topic Detection and Tracking Document Clustering Terminology Extraction Ontology Extraction Structured Information Knowledge Discovery + existing data

Information Extraction Analyzing unrestricted unstructured text Extracting specific structured information Enabling technology Converting text to a database (data mining) Summarization Compare: Text Understanding Information Retrieval

Example: MUC-terrorisme Input: San Salvador, 19 Apr 89. Salvadoran President-elect Alfredo Cristiani. condemned the terrorist killing of Attorney general Roberto Garcia Alvarado and accused the Farabundo Marti National Liberation Front (FMLN) of the crime. (...) Garcia Alvarado, 56, was killed when a bomb placed by urban guerrillas on his vehicle exploded as it came to a halt at an intersection in downtown San Salvador. Vice President-elect Francisco Merino said that when the attorney-general's car stopped at a light on a street in downtown San Salvador, an individual placed a bomb on the roof of the armored vehicle. (...) According to the police and Garcia Alvarado's driver, who escaped unscathed, the attorney general was traveling with two bodyguards. One of them was injured.

Output template: Incident: Date 19 APR 89 Incident: Location El Salvador: San Salvador Incident: Type Bombing Perpetrator: Individual ID urban guerrillas Perpetrator: Organization ID FMLN Perpetrator: Organization conf suspected or accused Physical target: description vehicle Physical target: effect some damage Human target: name Roberto Garcia Alvarado Human target: description attorney general Alvarado, driver, bodyguards Human target: effect death: alvarado,no injury: driver, injury: bodyguards

IEX System Architecture Local text analysis Lexical analysis tokenization, tagging, lemmatization Named Entity Recognition person name, company name, time expression, … Shallow Parsing (phrases and relations) Extraction Pattern matching of simple facts Integration of extracted facts into Larger facts (reference resolution) Additional facts (inference) Output template generation

Question Answering Give answer to question (document retrieval: find documents relevant to query) Who invented the telephone? Alexander Graham Bell When was the telephone invented? 1876

QA System: Shapaqa Parse question When was the telephone invented? Which slots are given? Verb invented Object telephone Which slots are asked? Temporal phrase linked to verb Document retrieval on internet with given slot keywords Parsing of sentences with all given slots Count most frequent entry found in asked slot (temporal phrase)

Shapaqa: example When was the telephone invented? Google: invented AND “the telephone” produces 835 pages 53 parsed sentences with both slots and with a temporal phrase is through his interest in Deafness and fascination with acoustics that the telephone was invented in 1876 , with the intent of helping Deaf and hard of hearing The telephone was invented by Alexander Graham Bell in 1876 When Alexander Graham Bell invented the telephone in 1876 , he hoped that these same electrical signals could

Shapaqa: example (2) So when was the phone invented? Internet answer is noisy, but robust 17: 1876 3: 1874 2: ago 2: later 1: Bell … System was developed quickly Precision 76% (Google 31%) International competition (TREC): MRR 0.45

Empiricism, analogy, induction, language A lightweight historical overview De Saussure: Any creation [of a language utterance] must be preceded by an unconscious comparison of the material deposited in the storehouse of language, where productive forms are arranged according to their relations. (1916, p. 165)

Lightweight history (2) Bloomfield: The only useful generalizations about language are inductive generalizations. (1933, p. 20). Zipf: nf 2 =k (1935), rf=k (1949)

Lightweight history (3) Harris: With an apparatus of linguistic definitions, the work of linguistics is reducible […] to establishing correlations. […] And correlations between the occurrence of one form and that of other forms yield the whole of linguistic structure. (1940) Hjelmslev: Induction leads not to constancy but to accident. (1943)

Lightweight history (4) Firth: A [linguistic] theory derives its usefulness and validity from the aggregate of experience to which it must continually refer. (1952, p. 168) Chomsky: I don't see any way of explaining the resulting final state [of language learning] in terms of any proposed general developmental mechanism that has been suggested by artificial intelligence, sensorimotor mechanisms, or anything else. (in Piatelli-Palmarini, 1980, p. 100)

Lightweight history (5) Halliday: The test of a theory [on language] is: does it facilitate the task at hand? (1985) Altmann: After the blessed death of generative linguistics, a linguist does no longer need to find a competent speaker. Instead, he needs to find a competent statistician. (1997)

Analogical memory-based language processing With a memory filled with instances of language mappings from text to speech, from words to syntactic structure, from utterances to acts , … With the use of analogical reasoning, Process new instances from input text, words, utterances to output speech, syntactic structure, acts

Analogy (1) sequence a sequence b sequence b’ sequence a’ is similar to is similar to maps to maps to

Analogy (2) sequence a sequence b ? sequence a’ is similar to is similar to maps to

Analogy (3) sequence n sequence b ? are similar to are similar to map to sequence a sequence f sequence a’ sequence n’ sequence f’

Memory-based parsing zo werd het Grand een echt theater … zo MOD/S wordt er … … zo gaat HD/S het … … en dan werd [ NP het DET <*> dus … … dan is het < *Naam> HD/SUBJ NP ] bijna erger … … ergens ene keer [ NP een DET echt <*> … … ben ik een echt MOD <*> maar … … een echt bedrijf HD/PREDC NP ] zo MOD/S werd HD/S [ NP het DET Grand HD/SUBJ NP ] [ NP een DET echt MOD theater HD/PREDC NP ]

Make data (1) #BOS 54 2 1011781542 0 zo BW T901 MOD 502 werd WW1 T304 HD 502 het VNW3 U503b DET 500 Grand*v N1 T102 HD 500 een LID U608 DET 501 echt ADJ9 T227 MOD 501 theater N1 T102 HD 501 . LET T007 -- 0 #500 NP -- SU 502 #501 NP -- PREDC 502 #502 SMAIN -- -- 0 #EOS 54

Make data (2) Given context, map individual words to function+chunk code: zo MOD O werd HD O het DET B-NP Grand HD/SU I-NP een DET B-NP echt MOD I-NP theater HD/PREDC I-NP

Make data (3) Generate instances with context: _ _ _ zo werd het Grand MOD - O _ _ zo werd het Grand een HD - O _ zo werd het Grand een echt DET - B-NP zo werd het Grand een echt theater HD/SU - I-NP werd het Grand een echt theater _ DET - B-NP het Grand een echt theater _ _ MOD - I-NP Grand een echt theater _ _ _ HD/PREDC - I-NP

Crash course: Machine Learning The field of machine learning is concerned with the question of how to construct computer programs that automatically learn with experience. (Mitchell, 1997) Dynamic process: learner L shows improvement on task T after learning. Getting rid of programming. Handcrafting versus learning. Machine Learning is task-independent .

Machine Learning: Roots Information theory Artificial intelligence Pattern recognition Took off during 70s Major algorithmic improvements during 80s Forking: neural networks, data mining

Machine Learning: 2 strands Theoretical ML (what can be proven to be learnable by what?) Gold, identification in the limit Valiant, probably approximately correct learning Empirical ML (on real or artificial data) Evaluation Criteria: Accuracy Quality of solutions Time complexity Space complexity Noise resistance

Empirical ML: Key Terms 1 Instances : individual examples of input-output mappings of a particular type Input consists of features Features have values Values can be Symbolic (e.g. letters, words, …) Binary (e.g. indicators) Numeric (e.g. counts, signal measurements) Output can be Symbolic (classification: linguistic symbols, …) Binary (discrimination, detection, …) Numeric (regression)

Empirical ML: Key Terms 2 A set of instances is an instance base Instance bases come as labeled training sets or unlabeled test sets (you know the labeling, not the learner) A ML experiment consists of training on the training set, followed by testing on the disjoint test set Generalisation performance ( accuracy, precision, recall, F-score ) is measured on the output predicted on the test set Splits in train and test sets should be systematic: n-fold cross-validation 10-fold CV Leave-one-out testing Significance tests on pairs or sets of (average) CV outcomes

Empirical ML: 2 Flavours Greedy Learning abstract model from data Classification apply abstracted model to new data Lazy Learning store data in memory Classification compare new data to data in memory

Greedy vs Lazy Learning Greedy: Decision tree induction CART, C4.5 Rule induction CN2, Ripper Hyperplane discriminators Winnow, perceptron, backprop, SVM Probabilistic Naïve Bayes, maximum entropy, HMM (Hand-made rulesets) Lazy: k -Nearest Neighbour MBL, AM Local regression

Greedy vs Lazy Learning Decision trees keep the smallest amount of informative decision boundaries (in the spirit of MDL, Rissanen, 1983) Rule induction keeps smallest number of rules with highest coverage and accuracy (MDL) Hyperplane discriminators keep just one hyperplane (or vectors that support it) Probabilistic classifiers convert data to probability matrices k-NN retains every piece of information available at training time

Greedy vs Lazy Learning Minimal Description Length principle: Ockham’s razor Length of abstracted model (covering core ) Length of productive exceptions not covered by core ( periphery ) Sum of sizes of both should be minimal More minimal models are better “ Learning = compression” dogma In ML, length of abstracted model has been focus; not storing periphery

Greedy vs Lazy Learning + abstraction - abstraction + generalization - generalization Decision Tree Induction Hyperplane discriminators Regression Handcrafting Table Lookup Memory-Based Learning

Greedy vs Lazy: So? Highly relevant to ML of NL In language data, what is core ? What is periphery ? Often little or no noise; productive exceptions (Sub-)subregularities, pockets of exceptions “ disjunctiveness” Some important elements of language have different distributions than the “normal” one E.g. word forms have a Zipfian distribution

ML and Natural Language Apparent conclusion: ML could be an interesting tool to do linguistics Next to probability theory, information theory, statistical analysis (natural allies) “ Neo-Firthian” linguistics More and more annotated data available Skyrocketing computing power and memory

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