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Natural Language Processing (NLP) Informally: NLP = computers handling ordinary language 6000-7000 languages exist. Important differences, more important.

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Presentation on theme: "Natural Language Processing (NLP) Informally: NLP = computers handling ordinary language 6000-7000 languages exist. Important differences, more important."— Presentation transcript:

1 Natural Language Processing (NLP) Informally: NLP = computers handling ordinary language 6000-7000 languages exist. Important differences, more important similarities Applications of NLP: to facilitate person-person communication: Machine Translation (MT), Summarisation,.. person-machine communication: Question-Answering, travel booking, car navigation,... NLP - sometimes also includes speech processing. Some slides from Kees Van Deemter

2 Text Language Technology Natural Language Understanding Natural Language Generation Speech Recognition Speech Synthesis Text Meaning Speech

3 Natural Language Understanding speech recognition (unless input is text) parsing word disambiguation determining overall meaning

4 Natural Language Generation Natural Language Generation: what information to convey how to distribute information across sentences how to express information in a sentence determine sentence melody etc.

5 Dialogue systems Question-Answering, travel booking, customer service Dialogue systems perform understanding and generation: they perform understanding to make sense of your utterances then they perform generation to produce a new utterance They are not very good!

6 European Association for Machine Translation, 1997 Machine Translation (MT) Translating texts from one natural language to another One of the very earliest pursuits in computer science MT has proved to be an elusive goal 1950s: Much money in USA, USSR, Britain, Italy, France 1966: Any task that requires real understanding of natural language is too difficult for a computer - Bar-Hillel Today a number of systems are available which produce output which, if not perfect, is of sufficient quality to be useful in a number of specific domains.

7 Natural Language is Notoriously Ambiguous Squad helps dog bite victim. Helicopter powered by human flies. American pushes bottle up Germans. Once-sagging cloth diaper industry saved by full dumps. Portable toilet bombed; police have nothing to go on. British left waffles on Falkland islands. Milk drinkers are turning to powder. Drunk gets nine months in violin case. Time flies like an arrow.

8 Natural Language is Notoriously Ambiguous Squad helps dog bite victim. Helicopter powered by human flies. American pushes bottle up Germans. Once-sagging cloth diaper industry saved by full dumps. Portable toilet bombed; police have nothing to go on. British left waffles on Falkland islands. Milk drinkers are turning to powder. Drunk gets nine months in violin case. Time flies like an arrow. (You should) time flies as you would (time) an arrow Time flies in the same way that an arrow would (time them) Time those flies that are like arrows Fruit flies like a banana each of above Time magazine travels straight when thrown

9 Natural Language is Notoriously Ambiguous Squad helps dog bite victim. Helicopter powered by human flies. American pushes bottle up Germans. Once-sagging cloth diaper industry saved by full dumps. Portable toilet bombed; police have nothing to go on. British left waffles on Falkland islands. Milk drinkers are turning to powder. Drunk gets nine months in violin case. Time flies like an arrow. (You should) time flies as you would (time) an arrow Time flies in the same way that an arrow would (time them) Time those flies that are like arrows Fruit flies like a banana each of above Time magazine travels straight when thrown Surprise for early researchers: Almost every utterance is highly ambiguous Alternative interpretations often not apparent to native speaker

10 Which Nouns do Adjectives Apply to? pretty little girls' school Does the school look little? Do the girls look little? Do the girls look pretty? Does the school look pretty?

11 Natural Language is Notoriously Ambiguous 1.mature students and staff (mature students) and staff rotten apples and oranges mature (students and staff) Two different syntactic analyses Two different MEANING REPRESENTATIONS 2.Everyone can win a gold medal [1 medal] Everyone can take a chocolate [8 chocolates] This is not evidently a matter of syntax Two different MEANING REPRESENTATIONS 3.Many nouns have many meanings: Trunk, bank, battery Syntactic Ambiguity Semantic Ambiguity

12 Metonymy (one thing stands for another) Ive read Shakespeare Chrysler announced record profits Metaphor More is up Prices have risen, climbed, skyrocketed Temperature has dipped, fallen Confidence has plummeted Popularity has jumped, soared Ive tried killing the process but it wont die. Its parent keeps it alive.

13 Anaphora Anaphora : pronouns refer back to things already introduced We gave the monkeys the bananas because they were hungry. We gave the monkeys the bananas because they were over-ripe. 1.After Mary proposed to John, they found a preacher and got married. 2.For the honeymoon, they went to Hawaii 3.Mary saw a ring through the window and asked John for it. 4.Mary threw a rock at the window and broke it.

14 Anaphora Dana dropped the cup on the plate. It broke. Dana was quite fond of a special blue cup. The cup had been a present from a close friend. Unfortunately, one day while setting a place at the table, Dana dropped the cup on the plate. It broke. Discourse has structure above the level of a sentence…

15 Discourse Understanding 1.A funny thing happened yesterday Introduces new focus space and Evaluates it 2.John went to a fancy restaurant Enables 3. 3.He ordered the duck Causes 4. 4.The bill came to $50 2-4 serve as Ground for the rest of the story; implies John ate the duck 5.John got a shock when he realized he had no money 6.He had left his wallet at home Explains 5. 5-6 enable 7 7.The waiter said it was all right to pay later 5-7 cause 8 8.He was very embarrassed by his forgetfulness

16 Discourse Understanding (is hard!) Alice: Lets go to the cinema. Bob: I have an exam tomorrow. Kid: Mommy, Im hungry. Mother: Have you finished all your homework?

17 Tricks for Language Understanding Exploit many constraints: meanings of individual words (lexicon) grammatical constraints (+case roles and verb categories) Discourse coherence constraints Language model Speaker model World model Pretty good models available for all except the world model

18 Machine Translation Difficulties When languages similar, one can hope that word-by-word translation preserves ambiguity When languages are very different, this is often not the case The example `open` shows that the problems arises even in English/German: on the door of a store (German: `offen`) on a banner in front of the store (German: `neu eroeffnet) Open {shop, market, question, position} – loose ice? Words dont map one to one It is necessary to model the situation in your mind (disambiguate), and then describe it in the other language So why not make the computer model it? Commonsense knowledge problem


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