Catching competitors: lexical evolution in diachronic corpora

class: center, middle, inverse, title-slide

# Catching competitors: lexical evolution in diachronic corpora
### Andres Karjus, Richard A. Blythe, Simon Kirby, Kenny Smith Centre for Language Evolution, University of Edinburgh
### CL2019, Cardiff, July 2019

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class: inverse
# All languages change

p {margin-bottom:0em}

</style>

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# All languages change

- The big picture: human languages evolve on a cultural timescale: 
    - individual utterance selection > language change > language evolution
    - a language -> another language(s)
--

- Massive centuries-spanning corpora compiled in the recent years open up an unprecedented avenue of possible investigations into language dynamics.
- Variant usage frequencies but also meaning (and change) using distributional semantics methods

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# All languages change

- What I'm interested in: as new words - e.g. neologisms & borrowings - are selected for, what happens to their older synonyms? Does direct competition always follow local frequency changes?
--

- Hypothesis: 
    - frequency increase in a word will lead to direct competition with (and possibly replacement of) near-synonym(s)
    - unless the lexical subspace experiences high communicative need. 
--

- Communicative need and communicative utility 
.small[(Givon 1982, McMahon 1994:194, Tomasello 1999:37, Regier et al. 2016, Gibson et al. 2017, Smith et al. 2017)]
- How much does communicative need drive language change?

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# Some technical challenges

- what data to test this on
- how to model changes in communicative need
- how to capture competition dynamics

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# Data

- Sample unique words from a corpus, with frequency increase `\(\ln\geq 2\)` between any 2 spans of 10y, occur in `\(\geq 2\)` years, `\(\geq\)` 100x; e.g.:

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---
class:inverse
# Quantifying competition

(the thing I want to predict)

---
# Quantifying competition

- Distributional semantics, meaning from data
<img src="img/tcm_anim.mov.gif" width="80%" />
- Embed targets into vector space (LSA) of preceding decade, compute semantic neighbors

---
# Quantifying competition

- Important: word occurrence probabilities sum up to 1; increase in x means decrease in y.
--

- The measure: *where* the probability mass gets equalized, i.e., target increase `\(\geq \sum_{}^{}\)` (neighbors' decreases). Either cosine distance, or n increasing neighbors.
- Indicates if the increasing target replaced semantically close word(s) (direct competition, obvious likely source of probability mass).

---
# An example semantic space

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---

- Example: _relativism_, increasing +13.2pmw between 1965-1974, 1975-1984:
--

- word | freq.change | cumsum(decr) | normd. dist
- _**relativism**_ &nbsp; +13.2 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; . &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .
--

- *marxism*   &nbsp;&nbsp; &nbsp; -5.68 &nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  5.68 &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; 0
--

- *thesis* &nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp; +9.00 &nbsp;&nbsp; &nbsp; &nbsp;&nbsp; 5.68 &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0.01
--

- *jacksonian* -11.64 &nbsp; &nbsp; 17.32>13.2 &nbsp; &nbsp; 0.03
- -
- *validity*
- *interpretation*

---
class: inverse
# Communicative need

(the predictor)

---
# Communicative need

- Model diachronic topical fluctuations by quantifying the frequency change of a word's topic.
- The topical-cultural advection model; proxy to communicative need
- _advection_: 'the transport of substance, particularly fluids, by bulk motion'
- Formalized as the _weighted mean of the log frequency changes of the relevant topic (context) words of the target word_

---
# How does this work?

- Generate a "topic" for each target word, consisting of _m_ associated context words
<img src="img/tcm_anim.mov.gif" width="80%" height="50%"/>
 - weighted mean frequency change of topic/context words e.g. _cafe_, _cappuchino_ .small[(these are removed from the list of neighbours)]

---

# How well does it work?

- Correlate the log frequency changes of all (sufficiently frequent) nouns between two time periods to their respective topical advection values
- describes ~20-40% variance in word frequency changes between the 20 decades in COHA
- Comparable results from LDA
- cf. Karjus et al., *Quantifying the dynamics of topical fluctuations in language* (to appear in Language Dynamics and Change) .small[preprint https://arxiv.org/abs/1806.00699 ]

---
class:inverse
# Results

---
# Results (COHA)

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(1890) closest: bluff 0.55 cosine neighbrs with increasing freq 26 ","yere (1890) closest: wat 0.59 cosine neighbrs with increasing freq 0 losers by 0.05 normd dist: wat mammy bin yit ","vertebrate (1890) closest: gen 0.63 cosine neighbrs with increasing freq 0 losers by 0.16 normd dist: gen gente organic alphabet ","sweater (1890) closest: tailor 0.46 cosine neighbrs with increasing freq 1 ","canton (1891) closest: twothirds 0.67 cosine neighbrs with increasing freq 5 losers by 0.21 normd dist: twothirds judiciary prerogative parliament ","beau (1892) closest: mrs 0.66 cosine neighbrs with increasing freq 8 losers by 0.23 normd dist: duc sackett batt earl impatiently rho mag nonsense ","cuban (1892) closest: british 0.54 cosine neighbrs with increasing freq 4 ","mammal (1892) closest: specimen 0.49 cosine neighbrs with increasing freq 8 ","trolley (1892) closest: cab 0.6 cosine neighbrs with increasing freq 6 losers by 0.16 normd dist: vehicle junction firstclass ","populist (1892) closest: poll 0.73 cosine neighbrs with increasing freq 1 losers by 0.11 normd dist: congressional whig minority ","sheik (1893) closest: knight 0.53 cosine neighbrs with increasing freq 4 ","deh (1893) closest: bet 0.73 cosine neighbrs with increasing freq 2 losers by 0.07 normd dist: sich hab ","gent (1894) closest: dem 0.79 cosine neighbrs with increasing freq 2 losers by 0.05 normd dist: dem hai tew yew ","automobile (1894) closest: steamship 0.54 cosine neighbrs with increasing freq 11 ","boomer (1894) closest: corporal 0.58 cosine neighbrs with increasing freq 0 losers by 0.05 normd dist: corporal apache ","pregnancy (1895) closest: recovery 0.61 cosine neighbrs with increasing freq 0 losers by 0.16 normd dist: recovery puerperal epidemic ","viceroy (1895) closest: ambassador 0.59 cosine neighbrs with increasing freq 2 losers by 0.11 normd dist: forthwith formally duke ","anther (1895) closest: grape 0.56 cosine neighbrs with increasing freq 1 losers by 0.05 normd dist: grape 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(1975) closest: monk 0.65 cosine neighbrs with increasing freq 2 losers by 0.09 normd dist: monk sacred protestant ","relativism (1975) closest: marxism 0.67 cosine neighbrs with increasing freq 1 losers by 0.03 normd dist: marxism jacksonian ","hispanic (1975) closest: overwhelmingly 0.64 cosine neighbrs with increasing freq 0 losers by 0.11 normd dist: overwhelmingly workingclass assemblyman upstate affluent ","deregulation (1975) closest: surcharge 0.6 cosine neighbrs with increasing freq 1 losers by 0.07 normd dist: surcharge subsidy ","wer (1977) closest: turtle 0.61 cosine neighbrs with increasing freq 3 losers by 0.2 normd dist: turtle ole nigger outa nothin spade kinda ","druid (1977) closest: demon 0.53 cosine neighbrs with increasing freq 3 losers by 0.06 normd dist: eternity divinity resurrection lan ","chee (1977) closest: chantry 0.63 cosine neighbrs with increasing freq 3 losers by 0.05 normd dist: chantry clone annoyed awhile ","shiite (1977) closest: leftist 0.68 cosine 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neighbrs with increasing freq 1 losers by 0.06 normd dist: junta nationalist gaullist ","mullah (1979) closest: martyr 0.61 cosine neighbrs with increasing freq 1 losers by 0.17 normd dist: martyr persecute fascist ","sandinista (1979) closest: faction 0.62 cosine neighbrs with increasing freq 2 losers by 0.13 normd dist: nationalist rhodesian ","dint (1980) closest: nothin 0.62 cosine neighbrs with increasing freq 0 losers by 0.04 normd dist: nothin hoss ","bane (1980) closest: hesitantly 0.59 cosine neighbrs with increasing freq 3 losers by 0.13 normd dist: hesitantly annoyed annoy uneasily shyly grownup instinctively politely ","ast (1980) closest: looseleaf 0.72 cosine neighbrs with increasing freq 0 losers by 0 normd dist: looseleaf ","meese (1980) closest: comptroller 0.62 cosine neighbrs with increasing freq 1 losers by 0.05 normd dist: comptroller breakin ","tartar (1981) closest: lord 0.57 cosine neighbrs with increasing freq 2 losers by 0.13 normd dist: spaniard tyrant senora 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dist: stockholder beneficiary devaluation ","aids (1983) closest: respiratory 0.6 cosine neighbrs with increasing freq 10 ","waterfowl (1983) closest: mammal 0.57 cosine neighbrs with increasing freq 9 ","hittite (1983) closest: owain 0.54 cosine neighbrs with increasing freq 1 losers by 0.09 normd dist: owain horseman ","berserker (1983) closest: rendezvous 0.53 cosine neighbrs with increasing freq 5 ","ceo (1983) closest: conglomerate 0.69 cosine neighbrs with increasing freq 18 ","perestroika (1983) closest: ideological 0.55 cosine neighbrs with increasing freq 9 ","glasnost (1983) closest: continuation 0.55 cosine neighbrs with increasing freq 0 losers by 0.08 normd dist: continuation socialism junta ","mage (1984) closest: stranger 0.55 cosine neighbrs with increasing freq 3 ","mitigation (1985) closest: utilization 0.55 cosine neighbrs with increasing freq 5 losers by 0.11 normd dist: utilization lender governmental monetary ","kif (1985) closest: grimly 0.6 cosine neighbrs with increasing freq 0 losers by 0.13 normd dist: grimly impatiently nothin sucker mister lander ","firebird (1985) closest: valerian 0.56 cosine neighbrs with increasing freq 2 losers by 0.09 normd dist: valerian miraculously ","wetland (1985) closest: safeguard 0.49 cosine neighbrs with increasing freq 2 ","fax (1986) closest: telegraph 0.63 cosine neighbrs with increasing freq 2 losers by 0.24 normd dist: telegraph relay classified telegram dial ","ecosystem (1986) closest: coastal 0.53 cosine neighbrs with increasing freq 8 ","goblin (1987) closest: owain 0.58 cosine neighbrs with increasing freq 3 losers by 0.15 normd dist: owain wizard madman ","supra (1987) closest: finding 0.5 cosine neighbrs with increasing freq 5 ","privatization (1987) closest: conservation 0.64 cosine neighbrs with increasing freq 1 losers by 0.08 normd dist: conservation reorganization ","multicultural (1987) closest: dual 0.54 cosine neighbrs with increasing freq 8 ","database (1987) closest: printer 0.58 cosine neighbrs with increasing freq 17 ","nonattainment (1988) closest: guideline 0.5 cosine neighbrs with increasing freq 2 ","sgt (1988) closest: fuckin 0.74 cosine neighbrs with increasing freq 1 losers by 0.05 normd dist: shooter ","disconfirm (1988) closest: statistical 0.62 cosine neighbrs with increasing freq 6 losers by 0.17 normd dist: coherent analytical definite ","downsize (1988) closest: inflationary 0.51 cosine neighbrs with increasing freq 1 losers by 0.09 normd dist: inflationary morale ","islamist (1988) closest: rightwing 0.74 cosine neighbrs with increasing freq 0 losers by 0 normd dist: rightwing ","maggot (1989) closest: pussy 0.65 cosine neighbrs with increasing freq 4 losers by 0.14 normd dist: pussy asshole goddamned bullshit though tit ","braise (1989) closest: shrimp 0.74 cosine neighbrs with increasing freq 39 ","bosnian (1989) closest: lebanese 0.78 cosine neighbrs with increasing freq 0 losers by 0 normd dist: lebanese ","incinerator (1989) closest: reactor 0.6 cosine neighbrs with increasing freq 0 losers by 0 normd dist: reactor ","multiculturalism (1989) closest: socialism 0.62 cosine neighbrs with increasing freq 0 losers by 0 normd dist: socialism ","ilex (1990) closest: bane 0.6 cosine neighbrs with increasing freq 0 losers by 0 normd dist: bane ","rapper (1990) closest: comedian 0.59 cosine neighbrs with increasing freq 0 losers by 0.18 normd dist: comedian actress repertory ","managed (1990) closest: voluntary 0.53 cosine neighbrs with increasing freq 5 losers by 0.06 normd dist: voluntary mandatory restructure continuation ","internet (1990) closest: syndicate 0.61 cosine neighbrs with increasing freq 3 losers by 0.09 normd dist: broadcasting ","cdrom (1990) closest: recording 0.55 cosine neighbrs with increasing freq 4 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englishman ","condor (1981) closest: primate 0.54 cosine neighbrs with increasing freq 0 losers by 0.08 normd dist: primate monkey ","software (1981) closest: subsidiary 0.53 cosine neighbrs with increasing freq 16 ","hilly (1982) closest: gabe 0.72 cosine neighbrs with increasing freq 0 losers by 0.03 normd dist: gabe anyhow dude outa ","contra (1982) closest: communist 0.62 cosine neighbrs with increasing freq 1 losers by 0.11 normd dist: communist syrian vietnamese cambodian ","mammy (1982) closest: stepmother 0.61 cosine neighbrs with increasing freq 3 losers by 0.14 normd dist: stepmother countess reproach grownup niece nag grieve blurt lute ","leveraged (1982) closest: lender 0.65 cosine neighbrs with increasing freq 9 losers by 0.19 normd dist: stockholder beneficiary ","kuwaiti (1982) closest: turkish 0.62 cosine neighbrs with increasing freq 0 losers by 0 normd dist: turkish ","buyout (1982) closest: dividend 0.7 cosine neighbrs with increasing freq 3 losers by 0.18 normd dist: stockholder beneficiary devaluation ","aids (1983) closest: respiratory 0.6 cosine neighbrs with increasing freq 10 ","waterfowl (1983) closest: mammal 0.57 cosine neighbrs with increasing freq 9 ","hittite (1983) closest: owain 0.54 cosine neighbrs with increasing freq 1 losers by 0.09 normd dist: owain horseman ","berserker (1983) closest: rendezvous 0.53 cosine neighbrs with increasing freq 5 ","ceo (1983) closest: conglomerate 0.69 cosine neighbrs with increasing freq 18 ","perestroika (1983) closest: ideological 0.55 cosine neighbrs with increasing freq 9 ","glasnost (1983) closest: continuation 0.55 cosine neighbrs with increasing freq 0 losers by 0.08 normd dist: continuation socialism junta ","mage (1984) closest: stranger 0.55 cosine neighbrs with increasing freq 3 ","mitigation (1985) closest: utilization 0.55 cosine neighbrs with increasing freq 5 losers by 0.11 normd dist: utilization lender governmental monetary ","kif (1985) closest: grimly 0.6 cosine neighbrs with increasing freq 0 losers by 0.13 normd dist: grimly impatiently nothin sucker mister lander ","firebird (1985) closest: valerian 0.56 cosine neighbrs with increasing freq 2 losers by 0.09 normd dist: valerian miraculously ","wetland (1985) closest: safeguard 0.49 cosine neighbrs with increasing freq 2 ","fax (1986) closest: telegraph 0.63 cosine neighbrs with increasing freq 2 losers by 0.24 normd dist: telegraph relay classified telegram dial ","ecosystem (1986) closest: coastal 0.53 cosine neighbrs with increasing freq 8 ","goblin (1987) closest: owain 0.58 cosine neighbrs with increasing freq 3 losers by 0.15 normd dist: owain wizard madman ","supra (1987) closest: finding 0.5 cosine neighbrs with increasing freq 5 ","privatization (1987) closest: conservation 0.64 cosine neighbrs with increasing freq 1 losers by 0.08 normd dist: conservation reorganization ","multicultural (1987) closest: dual 0.54 cosine neighbrs with increasing freq 8 ","database (1987) closest: printer 0.58 cosine neighbrs with increasing freq 17 ","nonattainment (1988) closest: guideline 0.5 cosine neighbrs with increasing freq 2 ","sgt (1988) closest: fuckin 0.74 cosine neighbrs with increasing freq 1 losers by 0.05 normd dist: shooter ","disconfirm (1988) closest: statistical 0.62 cosine neighbrs with increasing freq 6 losers by 0.17 normd dist: coherent analytical definite ","downsize (1988) closest: inflationary 0.51 cosine neighbrs with increasing freq 1 losers by 0.09 normd dist: inflationary morale ","islamist (1988) closest: rightwing 0.74 cosine neighbrs with increasing freq 0 losers by 0 normd dist: rightwing ","maggot (1989) closest: pussy 0.65 cosine neighbrs with increasing freq 4 losers by 0.14 normd dist: pussy asshole goddamned bullshit though tit ","braise (1989) closest: shrimp 0.74 cosine neighbrs with increasing freq 39 ","bosnian (1989) closest: lebanese 0.78 cosine neighbrs with increasing freq 0 losers by 0 normd dist: lebanese ","incinerator (1989) closest: reactor 0.6 cosine neighbrs with increasing freq 0 losers by 0 normd dist: reactor ","multiculturalism (1989) closest: socialism 0.62 cosine neighbrs with increasing freq 0 losers by 0 normd dist: socialism ","ilex (1990) closest: bane 0.6 cosine neighbrs with increasing freq 0 losers by 0 normd dist: bane ","rapper (1990) closest: comedian 0.59 cosine neighbrs with increasing freq 0 losers by 0.18 normd dist: comedian actress repertory ","managed (1990) closest: voluntary 0.53 cosine neighbrs with increasing freq 5 losers by 0.06 normd dist: voluntary mandatory restructure continuation ","internet (1990) closest: syndicate 0.61 cosine neighbrs with increasing freq 3 losers by 0.09 normd dist: broadcasting ","cdrom (1990) closest: recording 0.55 cosine neighbrs with increasing freq 4 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---

- R2=0.2. Clearer competition signal if: 
 - lower communicative need (advection; `\(\beta = 0.09\)`, `\(p<0.001\)`)
 - bursty series
 - smaller changes
 - a clear loser present
- Also controlled for, but all `\(p>0.05\)`: std of yearly frequencies &#8226; semantic subspace instability &#8226; uniqueness of the form &#8226; smallest edit distance among closest sem neighbors &#8226; polsemy &#8226; leftover prob. mass &#8226; age of word in corpus &#8226; target decade.

---
# Results (Estonian, German)

---
class: inverse
# Discussion

---
# Discussion

- Controlling for a range of factors, communicative need (operationalized by advection), describes a small amount of variance in competitive interactions between words
    - low advection words are more likely to replace a word with a similar meaning
--
    - high advection words: less likely (prob.mass from elsewhere)
--
    - tested against a random baseline
--

- Real effect is bigger?
    - Just words; messy population aggregates; messy ML
    - But also, this approach relies on a *lot* of parameters
--
- How does this relate to individual utterance selection processes?

---
# Work in progress: Scottish Twitter

- ~50k daily users; 72 days, ~1.5m words/day, >140k unique #s
<img src="cl2019talk_files/figure-html/unnamed-chunk-6-1.png" width="60%" style="display: block; margin: auto;" />

---
# Results (Scottish Twitter hashtags)

.small[ R2=0.12, advection p=0.34 ]
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increasing freq: 17 ","#gotfinale (19) closest: landing 0.64 cosine neighbrs with increasing freq: 9 ","#gameofthonesfinale (19) closest: prediction 0.57 cosine neighbrs with increasing freq: 10 ","#dogsatpollingstations (22) closest: @margaretferrier 0.57 cosine neighbrs with increasing freq: 18 ","#deniedmyvote (22) closest: @thehistoryguy 0.53 cosine neighbrs with increasing freq: 0 losers by 0.19 normd dist: @thehistoryguy leaflets complaint emailed @vonny_bravo leaflet ","#scottishcupfinal (23) closest: paradise 0.59 cosine neighbrs with increasing freq: 7 ","#doubleornothing (24) closest: becky 0.72 cosine neighbrs with increasing freq: 0 losers by 0.05 normd dist: becky bayley charlotte @mavengleen hardcore ","#trebletreble (24) closest: 🇱🇻 0.59 cosine neighbrs with increasing freq: 16 ","#bgt (24) closest: @emmerdale 0.56 cosine neighbrs with increasing freq: 7 ","#aewdon (24) closest: #mitb 0.75 cosine neighbrs with increasing freq: 6 ","#europeanelectionresults (25) closest: poll 0.63 cosine neighbrs with increasing freq: 14 losers by 0.24 normd dist: @electionmapsuk @vincecable campaigning tactical ballot @guyverhofstadt @lewis_goodall @nigel_farage #votegreen ","#uelfinal (28) closest: @scottishfa 0.55 cosine neighbrs with increasing freq: 4 losers by 0.07 normd dist: @scottishfa @skysportsnews @bbcsport @hampdenpark ","#permissionfromsajid (28) closest: @nicolasturgeon 0.51 cosine neighbrs with increasing freq: 10 ","#volunteersweekscot (31) closest: partnership 0.57 cosine neighbrs with increasing freq: 4 losers by 0.2 normd dist: partnership colleagues thrilled supportive karen engagement @fire_scot donation @nhsscotevent thanks ","#volunteersweek (31) closest: organising 0.56 cosine neighbrs with increasing freq: 11 ","#uclfinal (31) closest: chelsea 0.78 cosine neighbrs with increasing freq: 0 losers by 0.06 normd dist: chelsea @chelseafc #chears @arsenal ","#bgtfinal (32) closest: haunting 0.64 cosine neighbrs with increasing freq: 2 losers by 0.3 normd dist: haunting #btsonbgt finale @davidwalliams bts #soapawards ","#loveisland (33) closest: acc 0.67 cosine neighbrs with increasing freq: 32 ","#trumpukvisit (33) closest: @emilythornberry 0.56 cosine neighbrs with increasing freq: 6 losers by 0.26 normd dist: @ukstoptrump @davidlammy @bbcpolitics ","#loveisiand (34) closest: hahahah 0.68 cosine neighbrs with increasing freq: 0 losers by 0.12 normd dist: hahahah shaves ahaha honestly howlin hahahahah flack ano tht wank ","#worldenvironmentday (35) closest: coal 0.49 cosine neighbrs with increasing freq: 13 ","#scotedawards (35) closest: enthusiasm 0.55 cosine neighbrs with increasing freq: 11 ","#fifawwc (37) closest: #nationsleague 0.55 cosine neighbrs with increasing freq: 7 ","#wwessd (37) closest: shield 0.51 cosine neighbrs with increasing freq: 7 ","#scocyp (38) closest: @skybet 0.62 cosine neighbrs with increasing freq: 2 losers by 0.17 normd dist: @skybet kicks #nedeng holland ronaldo messi kane ","#canadiangp (38) closest: offside 0.48 cosine neighbrs with increasing freq: 8 ","#xboxe3 (39) closest: mario 0.77 cosine neighbrs with increasing freq: 7 losers by 0.26 normd dist: destiny stadia @bungie pokemon sonic #pokemondirect @playapex xd sims raid cod vr legends @easportsfifa ","#engsco (39) closest: korea 0.56 cosine neighbrs with increasing freq: 7 ","#ubie3 (40) closest: hyped 0.73 cosine neighbrs with increasing freq: 5 losers by 0.14 normd dist: halo gears minecraft lego @bungie pokemon infinite destiny ","#acestoassets (41) closest: meaningful 0.55 cosine neighbrs with increasing freq: 8 losers by 0.13 normd dist: inspires illness dementia crucial @nhs_lothian ","#aceawarenation (41) closest: communication 0.53 cosine neighbrs with increasing freq: 8 ","#belsco (41) closest: griffiths 0.72 cosine neighbrs with increasing freq: 3 losers by 0.17 normd dist: griffiths bain robbo header midfield @purefitbaw @jimspencesport ","#nintendodirecte3 (41) closest: fallout 0.79 cosine neighbrs with increasing freq: 0 losers by 0.02 normd dist: fallout cyberpunk jedi ubisoft ","#scotcharityawards (44) closest: @scottishedge 0.59 cosine neighbrs with increasing freq: 0 losers by 0.24 normd dist: @scottishedge @rtsscotland achievements celebration @roonthetoon ","#socceraid (46) closest: @toughmudder 0.48 cosine neighbrs with increasing freq: 6 ","#owbbt (46) closest: highland 0.43 cosine neighbrs with increasing freq: 4 ","#c4debate (46) closest: @peston 0.69 cosine neighbrs with increasing freq: 6 losers by 0.24 normd dist: @mrjamesob @bethrigby becomes leadsom mcvey esther ","#bbcournextpm (48) closest: #c4debate 0.71 cosine neighbrs with increasing freq: 1 losers by 0.25 normd dist: #c4debate @channel4 raab @telegraph dominic ","#ourgirlsourgame (49) closest: nigeria 0.55 cosine neighbrs with increasing freq: 6 ","#swnt (49) closest: @bbcsport 0.56 cosine neighbrs with increasing freq: 12 ","#scoarg (49) closest: @bbcsport 0.57 cosine neighbrs with increasing freq: 10 ","#edinburghpride (52) closest: homophobic 0.49 cosine neighbrs with increasing freq: 2 ","#glastonbury (58) closest: concert 0.67 cosine neighbrs with increasing freq: 1 losers by 0.08 normd dist: concert @pink ","#fyterfest (59) closest: #wwestompinggrounds 0.69 cosine neighbrs with increasing freq: 3 losers by 0.14 normd dist: #wwestompinggrounds @wwerollins seth becky ","#austriangp (59) closest: @officialbsb 0.52 cosine neighbrs with increasing freq: 31 ","#engusa (62) closest: #noreng 0.61 cosine neighbrs with increasing freq: 1 losers by 0.36 normd dist: #noreng @williamhill diving norway corners overs cricket ","#independenceday (64) closest: # 0.48 cosine neighbrs with increasing freq: 4 ","#itvdebate (69) closest: hustings 0.57 cosine neighbrs with increasing freq: 5 losers by 0.18 normd dist: hustings @glennbbc #bbcqt unelected @telegraph 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","#eurovision (13) closest: rock 0.45 cosine neighbrs with increasing freq: 66 ","#isl (13) closest: #totp 0.46 cosine neighbrs with increasing freq: 1 ","#aus (13) closest: cast 0.47 cosine neighbrs with increasing freq: 1 ","#nor (15) closest: poland 0.68 cosine neighbrs with increasing freq: 10 losers by 0.33 normd dist: poland belgium portugal finland georgia rep ","#gbr (17) closest: @rylan 0.6 cosine neighbrs with increasing freq: 17 ","#madonna (17) closest: anthem 0.57 cosine neighbrs with increasing freq: 20 ","#facupfinal (17) closest: @premierleague 0.64 cosine neighbrs with increasing freq: 0 losers by 0.01 normd dist: @premierleague @lfc @dcfcofficial ","#eurovisionsongcontest (17) closest: poland 0.63 cosine neighbrs with increasing freq: 23 ","#mitb (18) closest: fights 0.59 cosine neighbrs with increasing freq: 3 losers by 0.13 normd dist: fights boxing inoue taylor #taylorbaranchyk rounds fight ","#gameofthronesfinale (18) closest: kings 0.6 cosine neighbrs with increasing freq: 17 ","#gotfinale (19) closest: landing 0.64 cosine neighbrs with increasing freq: 9 ","#gameofthonesfinale (19) closest: prediction 0.57 cosine neighbrs with increasing freq: 10 ","#dogsatpollingstations (22) closest: @margaretferrier 0.57 cosine neighbrs with increasing freq: 18 ","#deniedmyvote (22) closest: @thehistoryguy 0.53 cosine neighbrs with increasing freq: 0 losers by 0.19 normd dist: @thehistoryguy leaflets complaint emailed @vonny_bravo leaflet ","#scottishcupfinal (23) closest: paradise 0.59 cosine neighbrs with increasing freq: 7 ","#doubleornothing (24) closest: becky 0.72 cosine neighbrs with increasing freq: 0 losers by 0.05 normd dist: becky bayley charlotte @mavengleen hardcore ","#trebletreble (24) closest: 🇱🇻 0.59 cosine neighbrs with increasing freq: 16 ","#bgt (24) closest: @emmerdale 0.56 cosine neighbrs with increasing freq: 7 ","#aewdon (24) closest: #mitb 0.75 cosine neighbrs with increasing freq: 6 ","#europeanelectionresults (25) closest: poll 0.63 cosine neighbrs with increasing freq: 14 losers by 0.24 normd dist: @electionmapsuk @vincecable campaigning tactical ballot @guyverhofstadt @lewis_goodall @nigel_farage #votegreen ","#uelfinal (28) closest: @scottishfa 0.55 cosine neighbrs with increasing freq: 4 losers by 0.07 normd dist: @scottishfa @skysportsnews @bbcsport @hampdenpark ","#permissionfromsajid (28) closest: @nicolasturgeon 0.51 cosine neighbrs with increasing freq: 10 ","#volunteersweekscot (31) closest: partnership 0.57 cosine neighbrs with increasing freq: 4 losers by 0.2 normd dist: partnership colleagues thrilled supportive karen engagement @fire_scot donation @nhsscotevent thanks ","#volunteersweek (31) closest: organising 0.56 cosine neighbrs with increasing freq: 11 ","#uclfinal (31) closest: chelsea 0.78 cosine neighbrs with increasing freq: 0 losers by 0.06 normd dist: chelsea @chelseafc #chears @arsenal ","#bgtfinal (32) closest: haunting 0.64 cosine neighbrs with increasing freq: 2 losers by 0.3 normd dist: haunting #btsonbgt finale @davidwalliams bts #soapawards ","#loveisland (33) closest: acc 0.67 cosine neighbrs with increasing freq: 32 ","#trumpukvisit (33) closest: @emilythornberry 0.56 cosine neighbrs with increasing freq: 6 losers by 0.26 normd dist: @ukstoptrump @davidlammy @bbcpolitics ","#loveisiand (34) closest: hahahah 0.68 cosine neighbrs with increasing freq: 0 losers by 0.12 normd dist: hahahah shaves ahaha honestly howlin hahahahah flack ano tht wank ","#worldenvironmentday (35) closest: coal 0.49 cosine neighbrs with increasing freq: 13 ","#scotedawards (35) closest: enthusiasm 0.55 cosine neighbrs with increasing freq: 11 ","#fifawwc (37) closest: #nationsleague 0.55 cosine neighbrs with increasing freq: 7 ","#wwessd (37) closest: shield 0.51 cosine neighbrs with increasing freq: 7 ","#scocyp (38) closest: @skybet 0.62 cosine neighbrs with increasing freq: 2 losers by 0.17 normd dist: @skybet kicks #nedeng holland ronaldo messi kane ","#canadiangp (38) closest: offside 0.48 cosine neighbrs with increasing freq: 8 ","#xboxe3 (39) closest: mario 0.77 cosine neighbrs with increasing freq: 7 losers by 0.26 normd dist: destiny stadia @bungie pokemon sonic #pokemondirect @playapex xd sims raid cod vr legends @easportsfifa ","#engsco (39) closest: korea 0.56 cosine neighbrs with increasing freq: 7 ","#ubie3 (40) closest: hyped 0.73 cosine neighbrs with increasing freq: 5 losers by 0.14 normd dist: halo gears minecraft lego @bungie pokemon infinite destiny ","#acestoassets (41) closest: meaningful 0.55 cosine neighbrs with increasing freq: 8 losers by 0.13 normd dist: inspires illness dementia crucial @nhs_lothian ","#aceawarenation (41) closest: communication 0.53 cosine neighbrs with increasing freq: 8 ","#belsco (41) closest: griffiths 0.72 cosine neighbrs with increasing freq: 3 losers by 0.17 normd dist: griffiths bain robbo header midfield @purefitbaw @jimspencesport ","#nintendodirecte3 (41) closest: fallout 0.79 cosine neighbrs with increasing freq: 0 losers by 0.02 normd dist: fallout cyberpunk jedi ubisoft ","#scotcharityawards (44) closest: @scottishedge 0.59 cosine neighbrs with increasing freq: 0 losers by 0.24 normd dist: @scottishedge @rtsscotland achievements celebration @roonthetoon ","#socceraid (46) closest: @toughmudder 0.48 cosine neighbrs with increasing freq: 6 ","#owbbt (46) closest: highland 0.43 cosine neighbrs with increasing freq: 4 ","#c4debate (46) closest: @peston 0.69 cosine neighbrs with increasing freq: 6 losers by 0.24 normd dist: @mrjamesob @bethrigby becomes leadsom mcvey esther ","#bbcournextpm (48) closest: #c4debate 0.71 cosine neighbrs with increasing freq: 1 losers by 0.25 normd dist: #c4debate @channel4 raab @telegraph dominic ","#ourgirlsourgame (49) closest: nigeria 0.55 cosine neighbrs with increasing freq: 6 ","#swnt (49) closest: @bbcsport 0.56 cosine neighbrs with increasing freq: 12 ","#scoarg (49) closest: @bbcsport 0.57 cosine neighbrs with increasing freq: 10 ","#edinburghpride (52) closest: homophobic 0.49 cosine neighbrs with increasing freq: 2 ","#glastonbury (58) closest: concert 0.67 cosine neighbrs with increasing freq: 1 losers by 0.08 normd dist: concert @pink ","#fyterfest (59) closest: #wwestompinggrounds 0.69 cosine neighbrs with increasing freq: 3 losers by 0.14 normd dist: #wwestompinggrounds @wwerollins seth becky ","#austriangp (59) closest: @officialbsb 0.52 cosine neighbrs with increasing freq: 31 ","#engusa (62) closest: #noreng 0.61 cosine neighbrs with increasing freq: 1 losers by 0.36 normd dist: #noreng @williamhill diving norway corners overs cricket ","#independenceday (64) closest: # 0.48 cosine neighbrs with increasing freq: 4 ","#itvdebate (69) closest: hustings 0.57 cosine neighbrs with increasing freq: 5 losers by 0.18 normd dist: hustings @glennbbc #bbcqt unelected @telegraph 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---
class:inverse
background-image: url(img/dany.png) 
background-size: cover

---
# Discussion (Scottish Twitter hashtags)

- Small set of targets (63)? Also tried with 101 (any) words.
- What do #s compete with anyway: only other #s, or all words?
- Timespans need more thought.
- Probably behave differently from "normal" words:
- &nbsp;&nbsp;&nbsp; *I watched Game of Thrones last night.*
--
 
- ?? *I watched Game of Thrones, GoT, the GoT finale last night.*
--
 
- &nbsp;&nbsp;&nbsp; *I watched #gameofthrones last night. &nbsp;&nbsp;&nbsp; #got #gotfinale #gameofthronesfinale*
 
---
class: inverse
# Conclusions

- Communicative need describes a small amount of variance in competitive interactions between words in diachronic corpora (but not Twitter).
- Presumably high communicative need facilitates the co-existence of similar words.
--

- Future directions: explore parameters; test with phrases; more corpora; implement with non-discrete spans and modern semantics models .small[ (e.g. temporal referencing, Dubossarsky et al 2019) ]; test this experimentally.
--

- Slides: andreskarjus.github.io/cl2019talk Twitter: @AndresKarjus

---
class: inverse

---
class: inverse
# Appendix

---
# All the parameters

- preprocessing choices (lemmatized; removed stopwords, numbers; homogenized compounds, spelling) 
- timespan (10y), min change (log()>2), min frequency in t2 (100), min occurrence years (2); twitter: 5d spans, at least once in span >=0.5% daily users 
- LSA k (100 dim), min freq (100), context window size (5), weighted 
- cosine distance: normalized by 1st neighbour 
- density: LSA-based, as mean of cos.sim of 2nd..10th neighbours 
- form similarity (restricted Damerau-Levenshtein, length-normalized) 
- filter out: leftover>100%, polysemy residuals>2 
- advection topic model k (75 words), min freq (100), context window size (10), weighted 
- polysemy model, context window size (2), weighted

</div>

---
# The competition measure

(COHA)
<img src="cl2019talk_files/figure-html/unnamed-chunk-8-1.png" width="100%" style="display: block; margin: auto;" />

---
# Notes on the competition measure

- We made sure to avoid auto-correlation between the advection measure and the dependent variable by filtering the neighbour lists of each target so that no topic word of the target (i.e. those with a PPMI>0 with the target, which are used to calculate the advection value) would be accounted for as a neighbor. This also makes sense from a semantic point of view: if two words, even if very similar occur near each other (e.g., "salt and pepper"), then it's less palusible that they would be competing against one another. Exceptions are certainly possible, such as meta-linguistic descriptions (e.g., "vapor, previously spelled as vapour"), but we assume these would be rare. 
- We also filtered out a small subset of target words with considerably higher-than-expected lexical dissemination (a proxy to polysemy, cf. Stewart & Eisenstein 2018), and those with a leftover probability mass >100% of its frequency. 
- We did not make use of the entire Corpus of Historical American English, as most of the 19th century decades are less balanced and smaller in size, the imbalance extends to the occurrence of non-standard dialects or registers in occasional year subcorpora. Similarly, we only used years after 1800 in the German corpus. The Estonian corpus only spans two decades, 1990s and 2000s, so all comparisons were done between these two, without accounting for exact starting years of each word's increase. 
- This approach certainly has limitations stemming from the imperfect nature of corpus tagging, composition balance, and vector semantics (LSA). We also disregard issues such as homonymy (although we control for polysemy in the targets) and multi-word units. 
- We ran randomized baselines to make sure the observed correlation with advection is not some (unknown) artefact of the machine learning models used here. This was done by randomizing similarity matrices, i.e. each target was assigned a random list of neighbors, with random similarity values (drawn from the concatenation of all similarity vectors). After hundreds of iterations, the advection variable would come out with a p-value below 0.05 in only about 5% of the runs (i.e., as expected with an `\(\alpha=0.05\)`). 
- Some outliers are removed on the bottom left plot on the distributions of distances and neighbors until probability mass equalized.

</div>

---
# The polysemy measure

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