acl acl2013 acl2013-161 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Pieter Wellens ; Remi van Trijp ; Katrien Beuls ; Luc Steels
Abstract: Fluid Construction Grammar (FCG) is an open-source computational grammar formalism that is becoming increasingly popular for studying the history and evolution of language. This demonstration shows how FCG can be used to operationalise the cultural processes and cognitive mechanisms that underly language evolution and change.
Reference: text
sentIndex sentText sentNum sentScore
1 Fluid Construction Grammar for Historical and Evolutionary Linguistics Pieter Wellens1, Remi 1VUB AI Lab Pleinlaan 2 1050 Brussels (Belgium) pieter | kat rien@ . [sent-1, score-0.121]
2 ai vub van Trijp2, Katrien Beuls1, Luc Steels2,3 2Sony Computer Science Laboratory Paris 6 Rue Amyot 75005 Paris (France) . [sent-2, score-0.092]
3 remi @ c s l Abstract Fluid Construction Grammar (FCG) is an open-source computational grammar formalism that is becoming increasingly popular for studying the history and evolution of language. [sent-5, score-0.357]
4 This demonstration shows how FCG can be used to operationalise the cultural processes and cognitive mechanisms that underly language evolution and change. [sent-6, score-0.212]
5 Ever increasing datasets, both in size and richness of anno- tation, are becoming available (Yuri et al. [sent-8, score-0.019]
6 , 2012; Davies, 2011), and linguists now have more powerful tools at their disposal for uncovering which changes have taken place. [sent-9, score-0.046]
7 In this demonstration, we present Fluid Construction Grammar (Steels, 2011, FCG), an open-source grammar formalism that makes it possible to also address the question of how these changes happened by uncovering the cognitive mechanisms and cultural processes that drive language evolution. [sent-10, score-0.312]
8 FCG combines the expressive power of feature structures and unification with the adaptivity and robustnes of machine learners. [sent-11, score-0.022]
9 2 Design Philosophy Fluid Construction Grammar is rooted in a cognitive-functional approach to language, which is quite different from a generative grammar such s ony . [sent-16, score-0.103]
10 A generative grammar is a model of language competence that licenses well-formed structures and rejects illformed utterances. [sent-21, score-0.103]
11 Such grammars often decide on the well- or ill-formedness of utterances by using a strong type system that defines a set of features and possible values for those features. [sent-22, score-0.022]
12 The burden of efficient and robust language processing with a generative grammar largely rests on the shoulders of the language processor. [sent-23, score-0.103]
13 In parsing, such a grammar tries to uncover as much meaning as possible from a given utterance rather than deciding on its grammaticality. [sent-25, score-0.103]
14 In the other direction, the grammar tries to produce intelligible utterances, which are well-formed as a side-effect if the grammar adequately captures the conventions of a particular language. [sent-26, score-0.226]
15 A cognitive-functional grammar can best be implemented without a strong type system because the set of possible features and values for them is assumed to be open-ended. [sent-27, score-0.103]
16 Efficient and robust language processing also becomes a joint responsibility of the grammar and the linguistic processor. [sent-28, score-0.139]
17 3 Reversible Language Processing As a construction grammar, FCG represents all linguistic knowledge as pairings of function and form (called constructions). [sent-29, score-0.137]
18 This means that any linguistic item, be it a concrete lexical item (see Figure 1) or a schematic construction, shares the same fundamental representation in FCG. [sent-30, score-0.067]
19 Each construction consists of two poles (a semantic/functional one and a syntactic/form one), each represented as a feature structure. [sent-31, score-0.136]
20 By using a separate semantic and syntactic pole, FCG allows the same construction to be efficiently parsed and produced by the same processing engine by simply changing the direction of application. [sent-32, score-0.101]
21 c A2s0s1o3ci Aatsiosonc fioartio Cno fmorpu Ctoamtiopnuatalt Lioin gauli Lsitnicgsu,i psatgices 127–132, Figure 1: Lexical construction for the proper noun “Kim” as shown in the FCG web interface. [sent-35, score-0.101]
22 All constructions are mappings between semantic (left) and syntactic feature structures (right). [sent-36, score-0.125]
23 FCG processing uses two different kinds of unification called match and merge. [sent-37, score-0.022]
24 going from meaning to form), the processor will consider a construction’s semantic pole as a set of conditions that need to be satisfied, and the syntactic pole as additional information that can be contributed by the constructi1o2/n6/1. [sent-42, score-0.106]
25 going from form to meaning), the roles of the poles are reversed. [sent-45, score-0.035]
26 Since FCG pays a lot of attention to the interaction between linguistic knowledge and processing, it makes it possible to investigate the consequences of particular aspects of grammar with regard to representation, production, parsing, learning and propagation (in a population of language users). [sent-46, score-0.172]
27 It is exactly this ability to monitor the impact of grammatical choices, that has sparked the interest of an increasingly wide audience of historical and evolutionary linguists. [sent-49, score-0.179]
28 With FCG, different historical stages can be implemented (which addresses questions about representation and processing) but FCG also comes bundled with a reflective learning framework (Beuls et al. [sent-50, score-0.15]
29 , 2012) for learning the key constructions of each stage. [sent-51, score-0.125]
30 That same architecture has proven to be adequately powerful to implement processes ofgrammaticalization so that Figure 2: Schematic overview of the experimental methodology for historical and evolutionary linguists. [sent-52, score-0.203]
31 The example here shows only two linguistic stages but there could be more. [sent-53, score-0.036]
32 actual linguistic change over time can be modeled (van Trijp, 2010; Beuls and Steels, 2013; Wellens and Loetzsch, 2012). [sent-54, score-0.036]
33 4 How to set up an evolutionary linguistics experiment in FCG? [sent-55, score-0.066]
34 As the FCG processor can both produce and parse utterances it is possible to instantiate not one but a set or population of FCG processors (or FCG agents) that can communicatively interact with each other. [sent-56, score-0.077]
35 Experiments in historical or evolutionary linguistics make use of this multiagent approach where all agents engage in situated pairwise interactions (language games) (Steels, 2012b). [sent-57, score-0.165]
36 In this systems demo we will focus on a recent experiment in the emergence of grammatical agreement (Beuls and Steels, 2013). [sent-58, score-0.123]
37 The language game consists of two agents in which one agent (the speaker) has to describe one or more (max three) objects in a scene to the other agent (the hearer). [sent-59, score-0.112]
38 It follows that without any grammatical marking it would be difficult (often impossible) for the hearer to figure out which words describe the same object and thus to arrive at a successful interpretation. [sent-61, score-0.102]
39 The hypothesis is that the introduction of agreement markers helps solve this ambiguity. [sent-62, score-0.058]
40 Next to setting up a language game script the methodology consists of operationalizing the linguistic strategies required for a population to bootstrap and maintain a particular linguistic system (in this case nominal agreement). [sent-63, score-0.175]
41 Examples of lin128 meta-layer processing repair diagnostic repair diagnostic dpiraognbolesmtic dpiraogbnolesmtic ! [sent-64, score-0.204]
42 " routine processing Figure 3: Reflective meta-layer architecture operating as part of an FCG agent/processor. [sent-66, score-0.022]
43 guistic systems already investigated include German case (van Trijp, 2012a; van Trijp, 2013), the grammatical expression of space (Spranger and Steels, 2012), the emergence of quantifiers (Pauw and Hilferty, 2012) and the expression of aspect in Russian (Gerasymova et al. [sent-67, score-0.155]
44 An experiment generally investigates multiple linguistic systems of increasing complexity where each system can, but need not, map to a stage along an attested grammaticalization pathway. [sent-69, score-0.141]
45 Most often a stage is introduced in order to gradually increase the complexity of the emergent dynamics. [sent-70, score-0.045]
46 Implementing and linking together all the components involved in a single system is a highly non-trivial undertaking and our methodology prescribes the following four steps to undertake for each system (see also Figure 2). [sent-72, score-0.018]
47 Reconstruction: A full operationalization of all the constructions (lexical and grammatical) involved in the chosen linguistic phenomena. [sent-73, score-0.161]
48 When multiple agents are initialized with these constructions they should be able to communicate successfully with each other. [sent-74, score-0.187]
49 This stage serves primarily to test and verify intuitions about the different linguistic systems. [sent-75, score-0.054]
50 Individual Learning: Implementation of learning algorithms (or re-use of existing ones) Figure 4: Meaningful marker strategy. [sent-76, score-0.018]
51 so that one agent can learn the constructions based on the input of another agent. [sent-77, score-0.147]
52 These learning operations are generally divided into diagnostics and repair strategies (see Figure 3). [sent-78, score-0.163]
53 Diagnostics continually monitor FCG processing for errors or inefficiencies and generate problems if they are found. [sent-79, score-0.021]
54 Repair strategies then act on these problems by altering the linguistic inventory (e. [sent-80, score-0.059]
55 Population Alignment: There exists a large gap between the cognitive machinary needed for learning an existing linguistic system (step 2) and bootstrapping, aligning and maintaining a complete linguistic system from scratch. [sent-83, score-0.099]
56 Grammaticalization: Moving from one linguistic system to another is the final step of the experiment. [sent-85, score-0.036]
57 The challenge is to find and implement the mechanisms that drive grammaticalization (Heine and Kuteva, 2007) in line with observed grammaticalization pathways. [sent-86, score-0.221]
58 As an example we’ll give a short sketch of one possible game as played in the meaningful marker strategy as schematically shown in Figure 4. [sent-87, score-0.112]
59 The sketch shows a context of four objects (O1 to O4), each described by three features. [sent-88, score-0.023]
60 The speaker chooses topic O1 O2 which, given his vocabulary (shown top right), results in uttering “shuqfon sizhic zabu”. [sent-89, score-0.055]
61 In order to explicitly communicate this linking the + speaker attaches the markers “-ti” and “-ta” so that their meaning is compatible with the objects they are linking as shown in the Figure. [sent-91, score-0.118]
62 Diamond shaped nodes represent lexical constructions, egg shaped nodes represent grammatical constructions and rectangular nodes represent semantic categories. [sent-93, score-0.213]
63 PREP] primes the category LOCATIVE RELATION which in turn primes both the [LOCATIVE RELATION] and [SPATIAL PHRASE] constructions. [sent-96, score-0.084]
64 Both of these constructions also require a semantic category [REFERENT]. [sent-97, score-0.125]
65 the hearer to arrive at a single non-ambiguous interpretation. [sent-98, score-0.068]
66 For more details we refer the reader to (Beuls and Steels, 2013) and the web demo at http://ai. [sent-99, score-0.025]
67 Reversible bidirectional processing, a single data representation for all linguistic knowledge, a reflective meta-layer architecture for learning and a multi-agent component for managing multiple interacting FCG instances. [sent-104, score-0.119]
68 FCG comes bundled with a library of heuristics and goal tests and with a bit of programming skills users can add new primitives easily. [sent-111, score-0.031]
69 Customizable construction inventory: By default, FCG stores all constructions in one large set. [sent-112, score-0.226]
70 One popular option is to organize constructions in smaller subsets (Beuls, 2011) like lexical, morphological, functional, etc. [sent-114, score-0.125]
71 Another option is to use networks (Wellens, 2011) that can learn co-occurrence relations between constructions and “prime” constructions when they are likely to apply (see Figure 5). [sent-115, score-0.25]
72 Interfaces to external repositories: FCG can connect to external repositories like Framenet (Baker et al. [sent-116, score-0.021]
73 Robustness: FCG continues operation as far as it can get even if some constructions do not apply (Steels and van Trijp, 2011). [sent-119, score-0.199]
74 Sup- plied with appropriate diagnostics and repair strategies FCG can even recover from errors (van Trijp, 2012b). [sent-120, score-0.163]
75 It is written in Common Lisp (CLOS) and compatible with most popular lisp implementations (SBCL, CCL, Lispworks, . [sent-124, score-0.035]
76 FCG builds on many existing and proven technologies and adds new innovations to the mix resulting in a user friendly, yet powerful and extensible framework for in-depth investigations in natural language phenomena. [sent-134, score-0.019]
77 Acknowledgments The FCG formalism is being developed at the Artificial Intelligence Laboratory of the Vrije Universiteit Brussel and the Sony Computer Science Laboratory in Paris. [sent-135, score-0.024]
78 Remi van Trijp is funded by the Sony Computer Science Laboratory Paris. [sent-138, score-0.074]
79 Agent-based models of strategies for the emergence and evolution of grammatical agreement. [sent-150, score-0.177]
80 N-grams and word frequency data from the corpus of historical american english (coha). [sent-169, score-0.058]
81 A language strategy for aspect: Encoding aktionsarten through morphology. [sent-172, score-0.025]
82 van Rijn, editors, the 31th Annual Conference of the Cognitive Science Society, pages 3023–3027. [sent-193, score-0.074]
83 Not as awful as it seems : Explaining german case through computational experiments in fluid construction grammar. [sent-239, score-0.407]
84 Linguistic assessment crite- ria for explaining language change: A case study on syncretism in German definite articles. [sent-248, score-0.021]
85 Priming through constructional dependencies: a case study in fluid construction grammar. [sent-252, score-0.407]
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Abstract: Derivational models are still an underresearched area in computational morphology. Even for German, a rather resourcerich language, there is a lack of largecoverage derivational knowledge. This paper describes a rule-based framework for inducing derivational families (i.e., clusters of lemmas in derivational relationships) and its application to create a highcoverage German resource, DERIVBASE, mapping over 280k lemmas into more than 17k non-singleton clusters. We focus on the rule component and a qualitative and quantitative evaluation. Our approach achieves up to 93% precision and 71% recall. We attribute the high precision to the fact that our rules are based on information from grammar books.
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