Semantic Maps: An Attempt at Catalogization and Comparison

Natalia Levshina F.R.S. – FNRS Université catholique de Louvain

Outline 1. Semantic maps: a typology 2. Semantic maps with R: a case study of European analytic

causatives

Semantic maps • a popular tool in constructional (lexical) and semantic

typology

Semantic maps • a popular tool in constructional (lexical) and semantic

typology • represent polyfunctionality of linguistic expressions

Semantic maps • a popular tool in constructional (lexical) and semantic

typology • represent polyfunctionality of linguistic expressions • a convenient tertium comparationis of form-function

mappings across languages

Haspelmath 2003: Datives

Haspelmath 2003: Datives

Main types of semantic maps

Connectivity (links between objects)

Proximity (distances between

objects)

Type-based

(pre-determined functions, frequencies do NOT count)

Manual/Automatic

Automatic

Token-based

(corpus exemplars, frequencies COUNT)

?

Automatic

Types of semantic maps

Connectivity (links between objects)

Proximity (distances between

objects)

Type-based

(pre-determined functions, frequencies do NOT count)

Manual/Automatic

Automatic

Token-based

(corpus exemplars, frequencies COUNT)

?

Automatic

Traditional maps • Non-hierarchical approaches - non-directional - directional • Hierarchical approaches

Traditional maps • Non-hierarchical approaches - non-directional - directional • Hierarchical approaches

Haspelmath 2003: Datives

Main principles • Nodes: A function is put on a map when there’s at least one

pair of languages which differ wrt. this function (Haspelmath 2003)

Main principles • Nodes: A function is put on a map when there’s at least one

pair of languages which differ wrt. this function (Haspelmath 2003)

• Links: the principle of connectivity (adjacency/contiguity): if a construction has more than one function, they should be connected (see van der Auwera 2013)

Example 1

Function A

Function B

Function C

Construction X

Example 2

Function A

Function B

Function C

Construction Y

Example 3

Function A

Function B

Function C

Construction Z

Example 4

Function A

Function B

Function C

Wrong: the connectivity principle is not observed!

Construction Q

A fix

Function A

Function B

Function C

Construction Q

Traditional maps • Non-hierarchical approaches - non-directional - directional • Hierarchical approaches

Narrog 2010: Goal-Recipient Domain

Traditional maps • Non-hierarchical approaches - non-directional - directional • Hierarchical approaches

Rakhilina et al. (In press) EMPTINESS

Serbian

Types of semantic maps

Connectivity (links between objects)

Proximity (distances between

objects)

Type-based

(pre-determined functions, frequencies do NOT count)

Manual/Automatic

Automatic

Token-based

(corpus exemplars, frequencies COUNT)

?

Automatic

CLICS (List et al. 2014): HAND

http://clics.lingpy.org/

Types of semantic maps

Connectivity (links between objects)

Proximity (distances between

objects)

Type-based

(pre-determined functions, frequencies do NOT count)

Manual/Automatic

Automatic

Token-based

(corpus exemplars, frequencies COUNT)

?

Automatic

Majid et al. 2008: cutting and breaking

Types of semantic maps

Connectivity (links between objects)

Proximity (distances between

objects)

Type-based

(pre-determined functions, frequencies do NOT count)

Manual/Automatic

Automatic

Token-based

(corpus exemplars, frequencies COUNT)

?

Automatic

Wälchli & Cysouw (2012): motion verbs

SEMANTIC MAPS WITH R

Analytic Causatives: Examples • English: make + Vinf, let + Vinf, have + Vinf, cause + to-Vinf • German: lassen + Vinf • Dutch: laten + Vinf, doen + Vinf • Russian: zastavljat’ “force” + Vinf, davat’ “give” + Vinf • French: faire + Vinf, laisser + Vinf • Romanian: face + să + Vsubj, lasă + să + Vsubj

DISTANCE- & TOKEN-BASED MAP

Multilingual corpus of film subtitles

An example of .srt format … 646 00:51:27,880 --> 00:51:32,920 <i>For always evil will look to find a foothold in this world.</i> 647 00:51:39,440 --> 00:51:42,603 Not good. Not good at all. 648 00:51:50,040 --> 00:51:51,326 Eww.

649 00:52:06,760 --> 00:52:09,081 Oh, no. Sebastian. 650 00:52:12,800 --> 00:52:13,847 Good gracious. 651 00:52:34,720 --> 00:52:35,767 Come on. …

Data set • All instances of ACs in 12 languages • 302 multilingual contexts, in which at least one doculect

contains an AC • Alignment: Jörg Tiedemann’s software subalign

Constructional types • ACs with various auxiliaries (e.g. lassen + V, fazer + V) • Transitives and ditransitives • Causative verb + Clause (e.g. dejar + que) • Causal prepositions (because of) • Causal and resultative subordinate clauses • Resultative cxs, e.g. make + Adj • Modals • Particles (niech in Polish) • Reflexives • Insubordination (que + Subj) • …

Matrix

Gower Distances cluster::daisy Situation (row) A EN: And we make them do it… ...or we kill them. make_V IT: E glielo facciamo fare ... o lo uccidiamo. fare_V CZ: Donutíme je to udělat, nebo je zabijeme. donutit_V Situation (row) B EN: Pick up someone my height and build and make them believe it is me. make_V IT: Individua una della mia corporatura e fa credere loro che sia io. fare_V CZ: Vyber někoho, kdo je mi podobný a přesvědč je, že jsem to já. Trans

Distance (A, B) = 1 – 2/3 ≈ 0.33

Multidimensional Scaling • Multidimensional Scaling of the distance matrix (isoMDS()

in MASS package) • 5 dimensions (stress < 0.15) • Visualization with bubble charts (googleVis) to interpret

the semantic functions • Other techniques to interpret and compare form-meaning

mapping…

Intepretation • The main principle: the closer two points on the map, the

more overlapping constructions they share across the languages. From the isomorphism principle it follows that the corresponding situations are more semantically similar (on average), since more authors of the doculects chose identical constructions to represent these causative situations.

faire + V: points

Kriging in fields::Krig()

lassen + V: points

Kriging

TYPE- & LINK-BASED MAPS

Matrix

Selecting Links • the total number of links between all 6 nodes is 15 (these

functions co-occur in at least one construction in the data set)

Selecting Links • the total number of links between all 6 nodes is 15 (these

functions co-occur in at least one construction in the data set) • However, not all of them are necessary because traditional

semantic maps allow for indirectly connected functions

Selecting Links • the total number of links between all 6 nodes is 15 (these

functions co-occur in at least one construction in the data set) • However, not all of them are necessary because traditional

semantic maps allow for indirectly connected functions • A solution: semmap() function in Rling (available upon

request) to induce a semantic map which observes the principle of connectivity

Selecting Links • the total number of links between all 6 nodes is 15 (these

functions co-occur in at least one construction in the data set) • However, not all of them are necessary because traditional

semantic maps allow for indirectly connected functions • A solution: semmap() function in Rling (available upon

request) to induce a semantic map which observes the principle of connectivity

• igraph package for graph building and visualization

Selecting Links • the total number of links between all 6 nodes is 15 (these

functions co-occur in at least one construction in the data set) • However, not all of them are necessary because traditional

semantic maps allow for indirectly connected functions • A solution: semmap() function in Rling (available upon

request) to induce a semantic map which observes the principle of connectivity

• igraph package for graph building and visualization • several solutions may be possible, even with the minimum

number of links (7) , use random sampling to check

A possible semantic map

Frequency of ‘co-constructification’

faire + V

lassen + V

HIERARCHICAL TREES

Tree

make + V and let + V

DIACHRONIC LINKS

Links (Narrog 2010) Edge sequence:

[1] Goal -> Beneficiary

[2] Goal -> Recipient

[3] Goal -> Purpose

[4] Recipient -> Patient

[5] Beneficiary -> Purpose

[6] Recipient -> Experiencer

[7] Recipient -> Possessor

[8] Purpose -> Cause

[9] Goal -> Patient

[10] Location -> Recipient

[11] Beneficiary -> Recipient

[12] Goal -> Location

Diachronic links

Narrog 2010: Goal-Recipient Domain

Narrog 2010: Goal-Recipient Domain

Recipient –Purpose link is not mentioned anywhere in the

data

Narrog 2010: Goal-Recipient Domain

Two missing links from Recipient to Experiencer and

Possessor (listed as uncontroversial links)

Let machines do the job!

Related tools • Componential Analysis: emic grids (cf. Evans 2010) • Cognitive Semantics: radial networks of polysemy • Distributional semantics: Semantic vector space visualizations

(Heylen, Wielfaert) • Variationist MDS or MCA exemplar-based maps (Levshina

2011; Levshina et al. 2013)