From the inbox…Subject: English; Computational Linguistics; Text/Corpus Linguistics: Computational Linguist, Bose Corporation, California, USAJob Location: California, USA Job Title: Senior Linguist; Job Rank: Computational Linguist

Specialty Areas: Computational Linguistics; General Linguistics; Text/Corpus Linguistics ; Required Language(s): English (eng)

Description: The Algorithms & Connected Experiences group is a growing interdisciplinary team inside Bose Research. We are pioneers working at the exciting crossroads of user experience and artificial intelligence (AI) with a mission to redefine the relationship between connected Bose products and its users. We conceive of and develop innovative prototypes using emergent cloud-based technology to explore strategic concepts for differentiated next generation music experiences. We drive hard to scale our successes to millions of users. Help our customers rediscover their love of music by transforming innovative ideas into reality.

Job description: We are seeking a Senior Linguist with practical experience bringing natural language understanding (NLU) systems into production. This candidate will be responsible for providing linguistic analysis to support quality assurance (QA) for data, team deliverables, grammar development, and user interface (UI/UX) design. The candidate will also develop annotation guidelines and manage team data collections. Example problem spaces include personalized and domain-specific NLU engines, intelligent music search, and conversational agents for spoken dialog systems.

Requirements: MS or Ph.D. in LinguisticsKnowledge of concepts and resources in computational linguistics: parsing, semantics, ontologies, dictionary management, etc.Native English speaker, fluent in at least one other language. Experience managing data annotation and data quality assurance efforts. Comfortable with defect detection, logging, and tracking. Can handle light scripting tasks to test and/or manipulate data

Nice to have: Experience with scripting or programming. Experience with context-free grammars. Experience with voice user interface (VUI) design. Experience with spoken dialog systems.

What’s in it for you: Be a part of and work with a top-notch multidisciplinary agile team. Build your career with people like you who want to solve problems and have fun together. Work on adding innovative and personalized features to world class Bose products that enhance user experience for millions of people. Excellent work-life balance and a continuous learning environment. Highly competitive package as well as a comprehensive benefits program.

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WHAT IS SEMANTICS?CM1

Symbols• We use symbols to represent things (entities, ideas, etc.)

• Words are symbols; so are numbers, icons, etc.• Meaning: associating symbols with what they represent

• and vice-versa• As with any other discipline, linguistics uses symbols

• IPA, parse trees, morphemes, sentences, punctuation, etc.• Semantics is all about symbols and their meaning

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Approaches to meaning

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• Referential (denotational)• Relating symbols to external objects• Logic, mathematics, models

• Psychological (mentalist)• Relating symbols to internal objects• AI, psycholinguistics, semantic representation

• Pragmatic (social)• Communication as a social activity• Interactions, agency, conventions• Text, argumentation, conversation, etc.

Compositionality• Representation of the meaning of (parts of) utterances• What does that (word/phrase/clause/sentence/conversa-

tion) mean? • To what extent can the meaning be derived?• To what extent can the meaning be arrived at

compositionally?• What components of linguistic processing contribute to

meaning?

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Caveats• Assumptions of generative linguistics

• Grammar is at the core of semantics• We should be able to build explanatory empirical models of semantics• Models are distinct from directly observable linguistic behavior (cf.

competence vs. performance)• Not everybody agrees on the facts• Accounts are often heterogeneous (cf. modularity)• Other related areas must necessarily be excluded

• Prosody• Pragmatics• Culture• Historical aspects of language change

• Widely divergent approaches, theories exist

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Referentiality• Most NP’s are referential:

That man drinking Postum is Fred.• but not all are:

The man who can lift this stone is stronger than an ox.Everyone here is a good syntactician.

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Propositional calculus• Representing propositions as atomic symbols

e.g. p: John is hungry.q: John eats Cheerios.r: Ottawa is the capital city of Canada.s: Paris is the capital city of France.t: Orem is not the center of the universe.

• Connectives: &, V, ¬, • p q• ¬p ¬q• s & q• ¬¬p• Truth value: true or false: r is true, t is false, p is ???

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Logical inferences• Modus Ponens:p q

p--------

q

• Modus Tollens:p q¬ q

---------¬ p

• Hypothetical syllogism:p qq r--------p r

• Disjunctive syllogism:p V q¬ p

--------q

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Formal logic and inferences• DeMorgan’s Laws

• ¬(ϕ V ψ) (¬ϕ & ¬ψ)• ¬(ϕ & ψ) (¬ϕ V ¬ψ)

• Conditional Laws• (ϕ → ψ) (¬ϕ V ψ)• (ϕ → ψ) (¬ψ → ¬ϕ)• (ϕ → ψ) ¬ (ϕ & ¬ψ)

• Biconditional Laws• (ϕ ↔ ψ) (ϕ → ψ) & (ψ → ϕ)• (ϕ ↔ ψ) (¬ϕ & ¬ψ) v (ϕ & ψ)

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Lexicalization• The way basic underlying concepts are lexically realized in a language

• Wide variation crosslinguistically• English: motion (V) + path (PP) vs. Romance languages

• He swam across the river.Il traversa la fleuve à la nage

• L1 verb L2 prepositional phraseL1 preposition L2 verb

• Limits exist: “flimped”, “moked” • Much research in semantic universals

Lexical relations• Synonyms• Antonyms• Homonyms• Polysemes• Meronyms• Hypernyms• Hyponyms

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Headlines and ambiguity• Iraqi head seeks arms • William Kelly was fed secretary • Police begin push to run down jaywalkers • Dealers will hear car talk at noon• Red tape holds up new bridges• Kids make nutritious snacks • Lansing residents can drop off trees • Farmer Bill Dies in House

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Basic set theory• Set: unordered collection of items

• They usually have some meaning in order to be grouped together• Members: could be individual items or tuples• V = {a, e, i, o, u} is the same as V = {o, i, e, a, u}• N = {1, 2, 3, 4, 5, 6, …}

• Sets can have an infinite number of items• C = { (0, 1), (1, 2), (2, 3), (3, 4) }• Tuples can be unordered () or ordered <>

• T = { <BYU, Cougars, Provo>, <UVU, Wolverines, Orem>, <UofU, Utes, SLC> }• Set membership: ∊

• a ∊ V• 1 ∉ V

• Set cardinality: | | (i.e. number of members)• |V| = 5• |N| = ∞• |T| = 3

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Basic set theory• Descriptions can be generalized:

• V = {a, e, i, o, u}• This is called an extensional description

• V = {x | x is a vowel in English}• This is called an intensional description

• The capital city of Utah?• Extensional definition: {SLC, Fillmore}• Intensional definition: the city where the governor lives, the legislature

enacts laws, etc.• Extension: rote listing of all items that exemplify that description• Intension: concepts related to the description in question• Give a definition for the word “dictator”

• Extensional• Intensional

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Basic set theory• The empty set: ø• Set identity: A = B

• They have exactly the same members• Singleton: set with only 1 item: X = {4}

• Cartesian product:A x B = {<x,y>| xA and yB}

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Set operations• Union of the sets A and B, denoted A ∪ B, is the set of all objects that are a member of A,

or B, or both. The union of {1, 2, 3} and {2, 3, 4} is the set {1, 2, 3, 4} .

• Intersection of the sets A and B, denoted A ∩ B, is the set of all objects that are members of both A and B. The intersection of {1, 2, 3} and {2, 3, 4} is the set {2, 3} .

• Set difference of U and A, denoted U \ A, is the set of all members of U that are not members of A. The set difference {1, 2, 3} \ {2, 3, 4} is {1} , while, conversely, the set difference {2, 3, 4} \{1, 2, 3} is {4} .

• When A is a subset of U, the set difference U \ A is also called the complement of A in U.

• Symmetric difference of sets A and B, denoted A △ B or A ⊖ B, is the set of all objects that are a member of exactly one of A and B (elements which are in one of the sets, but not in both). For instance, for the sets {1, 2, 3} and {2, 3, 4} , the symmetric difference set is {1, 4} . It is the set difference of the union and the intersection, (A ∪ B) \ (A ∩ B) or (A \ B) ∪ (B \ A).

• Cartesian product of A and B, denoted A × B, is the set whose members are all possible ordered pairs (a, b) where a is a member of A and b is a member of B. The cartesianproduct of {1, 2} and {red, white} is {(1, red), (1, white), (2, red), (2, white)}.

• Power set of a set A is the set whose members are all possible subsets of A. For example, the power set of {1, 2} is { {}, {1}, {2}, {1, 2} } .

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Venn diagrams

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What’s a model?

From the OED (paraphrased)• A set of designs (plans, elevations, sections, drawings, etc.) for

a structure• Something which accurately resembles or represents

something else, especially on a small scale• An archetypal image or pattern• An object or figure made in clay, wax, etc., as an aid to the

execution of the final form of a work of art• A simplified or idealized description or conception of a

particular system, situation, or process, often in mathematical terms, that is put forward as a basis for theoretical or empirical understanding, or for calculations, predictions, etc.; a conceptual or mental representation of something

• A person, or a work, that is proposed or adopted for imitation; an exemplar serving or intended to serve as a pattern for imitation

Scientific model• Representation of an object, phenomenon, or process in a

way that is:• Consistent• Logical• Describable• Reproducible• Informative• Unambiguous• Predictive• Interoperable across jargons• Comparable with other models

Models• Much of linguistics is about models• Much of semantics is all about models and meaning• We need to learn how to represent, use models• Models vary in complexity

• Most this semester will be much simpler than the real world• Models vary in granularity

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Models and theories

• Theories explain the mapping between the real world and models of it• Entities, objects, artifacts• Phenomena• Processes

Entailment• A entails B means:

• whenever A is true, B is true• the information that B conveys is contained in the information that A

conveys• a situation describable by A must also be describable by B• A & ¬B is contradictory

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Entailments?• This is yellow. This is a pencil.?This is a yellow pencil.

• This is big. This is a whale.?This is a big whale.

• Lee kissed Kim passionately.?Lee kissed Kim.?Kim was kissed.?Lee touched Kim with her lips.?Lee married Kim.?Kim kissed Lee.?Lee kissed Kim many times.

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Implicature• Sentence A implies sentence B: A suggests or conveys B,

B can be inferred from A• “The park rangers killed the bear.”✔“The bear is dead.”✘“The park rangers are evil assassins.”

• Conversational implicature• Did you enjoy the dinner?

We had pasta and no dessert.• Defeasibility: we can defeat implicatures but not

entailments (at least in English)• #The park rangers killed the bear, but the bear isn’t dead.

• Reinforceability: we can reinforce implicatures but not entailments

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Presupposition• Whether S or ¬S, P is in either case true (“entailment no

matter what”)• Nick admitted that the team lost. Nick said that the team lost.

• John regrets/believes that Mary went to the dance. (/ )• The captain thought/realized that the ship was in danger. ( /)

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Presupposition

• John regrets that he ate all the eggs.• John is sorry that he ate all the eggs.• John repents of having eaten all the eggs.• John is unhappy that he ate all the eggs.• John feels contrite about eating all the eggs.• John feels penitent about eating all the eggs.• John feels remorse for having eaten all the eggs.all presuppose:• John ate all the eggs.

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Coreference• *Behind [Mary]i [she]i heard the snake.• *[Herself]i is proud of [her]i.• If [that jerk]i calls, tell [Tom]i I’m busy tonight.• *[He]i insists that [the electrician]i found nothing wrong.• [Mary]i told [John]j that [they]/j+i/k/... were assigned clean-

up duty.

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C-command• Abbreviation for constituent-command• A structural relationship between two constituents (NP’s)• Def: NPa c-commands NPb if the first category above

NPa dominates NPb• Basis for many (but not all!) anaphoric relationships (even

crosslinguistically!)

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Levels of ambiguity (1)• Lexical

• I bought a pen.• She bought me a fly.

• Syntactic• I saw old men and women.• I saw her duck.• Visiting relatives can be tedious.

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Levels of ambiguity (2)• Semantic

• Everyone here speaks two languages.• Three men carried a piano.• You may not come to my party.• Judy wants to marry a Norwegian.• Every professor thinks she is busy.• A flag was hanging from every balcony.• Mary can’t sing.

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Ambiguity• More than one meaning possible for:

word/phrase/clause/sentence/utterance• Occurs at several different levels• Everyone loves somebody.

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A Mary

B Fred

C Mark

D Pat

…

Fred

ABC

ED

Various semantic relations• Contradiction• Paraphrase• Synonymy• Anomaly

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Two basic syn/sem techniques• Semantics is interpretive

• Morphology/syntax is prior• Map meaning from: syntaxsemantics• Most current linguistic theories

• Semantics is directly compositional• Developed in tandem with syntax• Map meaning while: syntax + semantics

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Denotation• We use [[ ]] as a shorthand for “the denotation of”• Maps a concept to its extension in the real world

• [[ the Ling 332 TA ]] = MichaelNix• [[ the US president ]] = Trump

• at least currently

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Signifié/Sinn/Sense vs. Signification/Bedeutung/Reference• The moon itself (reference)• The image of the moon on a lens (sense)

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Relations• It’s what ties everything/everyone together• “Greater than” is a relation

• Can be expressed as a set with tuples• G = { <2, 1>, <3, 1>, <4, 1>, <5, 1>, <3, 2>, <4, 2>, <5, 2>, …}• G = { <x, y> | x is greater than y}• xGy

• Reflexive relation iff, for all a, <a, a> ∊ R (i.e. aRa)• Symmetric relation iff whenever <a, b> ∊ R then <b, a> ∊

R (i.e. aRb bRa)• Transitive relation iff whenever <a, b> ∊ R and <b, c> ∊ R

then <a, c> ∊ R (i.e. aRb & bRc aRc)

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Relations• Given set A, set B, we can set up a relation R such that:

• it picks out some members of the set A X B

• it is represented as aRb• What is R in: 2R6, 3R9, 0R0, 100R300, .4R1.2?

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Functions• function: two-place relation that maps one symbol onto

another• domain range• was-born-in(Fred) = 1955• height-of(Sally) = 5 feet, 2 inches• capital-city(Rwanda) = Kigali

• Can be represented in lots of different ways

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Relating set theory to linguistics• Sentence: subject + predicate• Meaning of a sentence: assert something about the

relationship between the subject and the predicate• VP’s: sets (!)

• … is a dog• D = { Fido, Snoopy, Rover, Happy, …}• Fido is a dog. T or F? Solution: is Fido ∊ D?

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Grammatical fragments• Syntax• Semantics• The mapping between them

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Specifying types• t: truth value {0,1}• e: entity, individual• Any 2-tuple with these or their result

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The interpretation procedure• Diagram the syntax w/rt the PS rules• In bottom-up fashion:

• Build up denotations via function composition

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Predicate expressions• Formalizing semantic relations and their representation

• Predicate names: lexical item• Entities: variables (e.g. x, y, z)

• Multiple nesting is possible•

When John is hungry he eats Cheerios.hungry(J)eats(J,x) & cheerios(x) =if John is hungry then John eats x, where entity x is cheerios

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