(Upcoming seminars are visible in the calendar.)
The view of NLs as codes mediating a mapping between "expressions" and the world is abandoned to give way to a view where utterances are seen as actions aimed to locally and incrementally alter the affordances of the context. Such actions employ perceptual stimuli composed not only of "words" and "syntax" but also elements like visual marks, gestures, sounds, etc. Any such stimuli can participate in the domain-general processes that constitute the "grammar", whose function is the dynamic categorisation of various perceptual inputs and their integration in the process of generating the next action steps. Given these assumptions, a challenge that arises is how to account for the reification of such processes as exemplified in apparent metarepresentational practices like quotation, reporting, citation etc. It is argued that even such phenomena can receive adequate and natural explanations through a grammar that allows for
the ad hoc creation of occasion-specific content through reflexive mechanisms.
The talk is based on extensive corpus work dealing with the interaction of gesture and speech in natural route-description dialogues. The issue discussed is how non-regimented gesture and speech processes can be modelled in a formal system. The main argument in the talk is that this cannot be achieved in structural paradigms currently in use. The proposal is to turn instead to process algebras in the tradition of Milner's pi-calculus. The special algebra discussed in the talk is a newly developed hybrid lambda-psi calculus which can transport typed lambda-expressions over communicating input-output channels. Central for the account is the notion of agent: Agents encode speech information, gesture information or both. They can put information on channels and send it to other channels or take information from others and combine it with the information they have. Speech-gesture interaction is conceptualised via channel interactions of this sort. Interactions are allowed, postponed or blocked using a typing system. Successful communication among agents leads to a multi-modal meaning representation serving as logical form.
Rieser, H. (2014). Gesture and Speech as Autonomous Communicating Processes. Talk at the Stuttgart Workshop on "Embodied meaning goes public". Stuttgart University, December 2014
Rieser, H. (2015). When Hands Talk to Mouth. Gesture and Speech as Autonomous Communicating Processes. Proceedings of Semdial 2015, Gothenburg
Rieser, H. (2017). A Process Algebra Account of Speech-Gesture Interaction. Preliminary version. Ms, Bielefeld University
Formal semantics comprises a plethora of theories which interpret natural language through the use of di¿erent ontological primitives (e.g. possible worlds, situations, individual concepts, unanalyzable propositions). The ontological relations between these theories are, today, still largely unexplored. In particular, it remains unclear whether the basic objects of some of these theories can be coded in terms of objects from other theories (s.t. phenomena which are modeled by one theory can also be modeled by the other theories) or whether some of these theories can even be reduced to ontologically poor(er) theories (e.g. extensional semantics) which do not contain ¿special¿ objects like possible worlds.
This talk surveys my recent work on ontological reduction relations between formal semantic theories. This work shows that, more than preserving the modeling success of the reduced theory, some reductions even improve upon the theory's modeling adequacy or widen the theory's modeling scope. My talk illustrates this observation by two examples: (i) the relation between Montague-/possible world-style intensional semantics and extensional semantics (cf. Liefke and Sanders 2016), and (ii) the relation between intensional semantics and situationbased single-type semantics (cf. Liefke and Werning, in revision). The first relation established through the use of associates from higher-order recursion theory.
SlidesWhile general knowledge of the world plays a role in language use,
language processing in humans is also guided by formal intuitions about
linguistic representation. In this talk, I discuss research results in finding the boundaries between world knowledge and formalism-driven intuitions and situate them in the context of a larger research program in computational psycholinguistics.
The first result focuses on the semantics of predicates and their arguments and how they are interpreted by the human processor. English-speaking human raters judge doctors as more appropriate givers of advice than recipients and lunches as much more appropriate objects of "eat" than subjects. One of my recent projects resulted in the development of vector-space and neural network models of predicate-argument relations that model that succeed in achieving high correlations with human ratings.
The second result is about the interaction of world knowledge with higher order semantics. English-speakers tend to judge that the sentence "every child climbed a tree" refers to more than one tree, while "every jeweller appraised a diamond" is comparatively more likely to refer to a single diamond, based on their knowledge of trees and diamonds. Recent experimental results in the literature are ambivalent on the extent to which formal structure affects the power of world knowledge to influence these judgements. In response to this, I describe a recent judgement study I conducted using German scrambling that suggests a significant effect of formal representation on the plural interpretation of an object argument given a universally-quantified subject.
Both of these research efforts reveal underlying questions about the
influence of world knowledge on linguistic representations and suggest
ways to answer them.
February 1: Mathieu Lafourcade "Games with a Purpose: The JeuxdeMots project"
Human-based computation is an approach where some steps of a computation is outsourced to humans. Games with a purpose (GWAPs) are games aiming at resolving puzzled or collecting data, where humans still outperform machines.
The JeuxDeMots (JDM) projects is a web-based associative GWAP where people are invited to play on various lexical and semantic relations between terms. The aim of this project is to build a large lexico-semantic network, with various relations types and word refinements (word usages).
Text semantic analysis is the main application for exploiting this resource, however the use as a tool for providing help in the case of the "tip of the tongue" phenomenon is also fruitful. This presentation will present the principles behind the JDM project, as well has the results achieved so far (around 1 million terms for 67 million relations). The following aspects will be discussed: the interaction between the various games of the JDM environment, some inference mechanisms of relations and rules, word polarity and sentiments, and some ethical aspects. Some specific aspects of the JDM lexical network are detailed, such as : refinements, aggregated terms, inhibitory relations and relation annotations.
M. Lafourcade, N. Le Brun, and A. Joubert (2015) Games with a Purpose (GWAPS) ISBN: 978-1-84821-803-1 July 2015, Wiley-ISTE, 158 p.
M. Lafourcade, A. Joubert (2015) TOTAKI: A Help for Lexical Access on the TOT Problem. In Gala, N., Rapp, R. et Bel-Enguix, G. éds. (2015), Language Production, Cognition, and the Lexicon. Festschrift in honor of Michael Zock. Series Text, Speech and Language Technology XI. Dordrecht, Springer. 586 p. 140 illus. ISBN: 978-3-319-08042-0. (pp. 95-112)
In this paper, we attempt to answer the vexing question why it should be the case that only certain types of noun meanings exhibit mass/count variation in the lexicalization of their semantic properties, while others do not. This question has so far remained unanswered, or been set aside. We will do so by focusing on the role of context-sensitivity (already highlighted in recent theories of the mass/count distinction), and argue that it gives rise to a conflict between two pressures that influence the encoding of noun meanings as mass or count, one stemming from learnability constraints (reliability) and the other from constraints on informativeness (individuation). This will also lead us to identifying four semantic classes of nouns, and to showing why variation in mass/count encoding is, on our account, to be expected to occur widely in just two of them. Context-sensitivity forces a choice between prioritizing individuation, which aligns with count lexicalization, and prioritizing consistency, which aligns with mass lexicalization.
We present a divide-and-conquer algorithm for parsing
context-free languages efficiently. Our algorithm is an instance
of Valiant's (1975), who reduced the problem of parsing to matrix
multiplications. We show that, while the conquer step of
Valiant's is O(n³), it improves to O(log² n) under certain
conditions satisfied by many useful inputs.
One observes that inputs written by humans generally satisfy
those conditions. Thus, there appears to be a link between the
ability for a computer to efficiently parse an input in parallel
and the ability for a human to comprehend such an input.
With established recognition of the endemic context-relativity of language, it is now generally accepted that both parsing and production involve incremental context-relative decisions, requiring the concepts of both evolving contents and evolving contexts. Researchers across semantics, pragmatics, psycholinguistics, and computational linguistics are duly turning to the challenge of modelling language in terms that are compatible with such incrementality. Yet formal models of language remain largely grounded in the static terms of licensing sentential string- interpretation pairings reflecting only concepts such as compositionality, with little or no reflection of a time-linear process of information growth.
In this talk, I start by showing why linguists cannot avoid the challenge of defining grammar formalisms to reflect the dynamics of conversational dialogue, and how in order to achieve this, every aspect of linguistic knowledge needs to be recast as procedures for on-line incremental and predictive word-by-word understanding/production. I shall then briefly sketch the action-based Dynamic Syntax (DS) system to demonstrate its explanatory potential, by modelling what have been taken as canonical exemplars of semantic-independent syntactic processes, which in DS are all expressed in terms of incremental parsing/generation actions. I will show in passing how the resulting system, despite the lack of any conventional notion of syntax, nonetheless has the power to express both universal structural constraints and yet cross-language variability. Part of this will include the Directed Acyclic Graph characterisation of context as developing in lockstep with the evolving yet revisable content, demonstrating the system-internal potential for self/other-correction. The dynamics of conversational dialogue interactions will then emerge as the immediate consequence of this perspective on language; and I will briefly illustrate how this potential for interaction underpins all types of language-internal licensing constraint: syntactic, semantic, morphosyntactic and phonological.
I shall then turn to setting this perspective within the Predictive Processing (PP) model of cognition (Clark 2016), whose architectural properties the DS concept of language matches almost point by point. Like perception in the PP model, the DS grammar is a "fundamentally action-oriented" set of procedures, grounded in predictive processing resources shared by speakers (action) and hearers (perception) alike and "executed using the same basic computational strategy" leading to effects of interactive coordination without any need to invoke mind-reading or propositional inference. The result is that linguistic processing, perception, action, and thought are predicted to be "continuously intermingled" yielding representational updates "tailored to good enough online controls rather than aiming for rich mirroring". Instead, such updates are accomplished due to a strong version of affordance competition since the brain ¿continuously computes multiple probabilistically inflected possibilities for action¿ in a cost-effect balancing dynamic, with possibilities progressively winnowed down, allowing for possible revision, to yield at least one output in any successful outcome. To this set of characteristics (Clark 2016 p. 251), we have only to add the potential for interaction which such a language system predicts as default, and a wholly different perspective on language evolution opens up. Language can now be seen as an emergent and evolving system with manifest potential for consolidating cross-individual interactions, hence group effects, without ever having to invoke high-level inferences as external, "designer"-imposed motivation for such consolidation, this a dynamic for which language change already provides robust motivation.
Natural language meanings allow speakers to encode important real-world distinctions, but corpora of grounded language use also reveal that speakers categorize the world in different ways and describe situations with different terminology. To learn meanings from data, we therefore need to link underlying representations of meaning to models of speaker judgment and speaker choice. This paper describes a new approach to this problem: we model variability through uncertainty in categorization boundaries and distributions over preferred vocabulary. We apply the approach to a large data set of color descriptions, where statistical evaluation documents its accuracy. The results are available as a Lexicon of Uncertain Color Standards (LUX), which supports future efforts in grounded language understanding and generation by probabilistically mapping 829 English color descriptions to potentially context-sensitive regions in HSV color space.
joint work with Brian McMahan.
A set of brain regions on the lateral surfaces of left frontal, temporal, and parietal cortices robustly respond during language comprehension and production. Although we now have strong evidence that this language network is spatially and functionally distinct from brain networks that support other high-level cognitive functions, the internal structure of the language network remains poorly understood. Deciphering the language network's architecture includes i) identifying its component parts, and ii) understanding the division of labor among those components in space and time. I will first present evidence that all language regions closely track linguistic input. I will then argue that some of the traditional "cuts" that have been proposed in the literature (e.g., based on the size of the linguistic units, based on the distinction between storage and computation, or based on syntactic category) do not seem to be supported by the available evidence. Even aspects of language that have long been argued to preferentially, or selectively, rely on a specific region within the language network (e.g., syntactic processing being localized to parts of Broca¿s area) appear to be distributed across the network. Further, the very same regions that are sensitive to syntactic structure in language show sensitivity to lexical and phonological manipulations. This distributed nature of language processing is in line with much current theorizing in linguistics and the available behavioral psycholinguistic data that show sensitivity to contingencies spanning sound-, word- and phrase-level structure. Time permitting, I will talk about recent work on decoding single word meanings and more complex meanings from the neural activity in the language network, and speculate that the organizing principles of the language network may have to do with meaning.
Finding explanations for the observed variation in human languages is the primary goal of linguistics, and promises to shed light on the nature of human cognition. One particularly attractive set of explanations is functional in nature, holding that language universals are grounded in the known properties of human information processing. The idea is that grammars of languages have evolved so that language users can communicate using sentences that are relatively easy to produce and comprehend. In this talk, I summarize results from explorations in two linguistic domains, from an information-processing point of view.
First, I consider communication-based origins of lexicons of human languages. Chomsky has famously argued that this is a flawed hypothesis, because of the existence of such phenomena as ambiguity. Contrary to Chomsky, we show that ambiguity out of context is not only not a problem for an information-theoretic approach to language, it is a feature. Furthermore, word lengths are optimized on average according to predictability in context, as would be expected under an information theoretic analysis. We then apply this simple information-theoretic idea to a well-studied semantic domain: words for colors. And finally, I show that all the world's languages that we can currently analyze minimize syntactic dependency lengths to some degree, as would be expected under information processing considerations.
Piantadosi, S.T., Tily, H. & Gibson, E. (2012). The communicative function of ambiguity in language.Cognition 122: 280-291.
Piantadosi, S.T., Tily, H. & Gibson, E. (2011). Word lengths are optimized for efficient communication.Proceedings of the National Academy of Sciences 108(9): 3526-3529.
Futrell, R., Mahowald, K., & Gibson, E. (2015). Large-scale evidence of dependency length minimization in 37 languages. Proceedings of the National Academy of Sciences 112(33): 10336-10341. doi: 10.1073/pnas.1502134112.
Although many animal species have the ability to generate complex thoughts, only humans can share such thoughts with one another, via language. My research aims to understand i) the system that supports our linguistic abilities, including its neural implementation, and ii) the relationship between the language system and the rest of the human cognitive arsenal. I use behavioral, fMRI, and genotyping methods in healthy adults and children, intracranial recordings from the cortical surface in patients undergoing pre- or intra-surgical mapping (ECoG), and studies of individuals with developmental and acquired damage.
I will begin by introducing the "language network", a set of interconnected brain regions that support language comprehension and production. With a focus on the subset of this network dedicated to high-level linguistic processing, I will then consider the relationship between language and non-linguistic cognition. Based on data from fMRI studies and investigations of patients with severe aphasia, I will argue that the language network is functionally selective for language processing over a wide range of non-linguistic processes that have been previously argued to share computational demands with language, including arithmetic, executive functions, music, and action/gesture observation. This network plausibly stores our linguistic knowledge, which can be used for both interpreting and generating linguistic utterances. Time permitting, I will speculate on the relationship between the language network and other networks, including, critically, the domain-general executive system, and the system that supports social cognition.
Traditional linguistic models of syntax and language processing have assumed an error-free process of language transmission. But we know that this is not the case: people often make errors in both language production and comprehension. This has important ramifications for both models of language processing and language evolution. I first show that language comprehension appears to function as a noisy channel process, in line with communication theory. Given si, the intended sentence, and sp, the perceived sentence we propose that people maximize P(si | sp ), which is equivalent to maximizing the product of the prior P(si) and the likely noise processes P(si → sp ). I show how this simple formulation can explain a wide range of language processing phenomena, such as people’s interpretations of simple sentences, some aphasic language comprehension effects, and the P600 in the ERP literature. Finally, I discuss how thinking of language as communication in this way can explain aspects of the origin of word order, most notably that most human languages are SOV with case-marking, or SVO without case-marking.
Gibson, E., Bergen, L. & Piantadosi, S. (2013). The rational integration of noisy evidence and prior semantic expectations in sentence interpretation. Proceedings of the National Academy of Sciences, 110(20): 8051-8056. doi: 10.1073/pnas.1216438110.
Gibson, E., Piantadosi, S., Brink, K., Bergen, L., Lim, E. & Saxe, R. (2013). A noisy-channel account of cross-linguistic word order variation. Psychological Science, 4(7): 1079-1088. doi: 10.1177.
Gibson, E., Sandberg, C., Fedorenko, E., Bergen, L., & Kiran, S. (2015). A rational inference approach to aphasic language comprehension. Aphasiology. doi: 10.1080/02687038.2015.1111994.
The talk focuses on demonstratives of manner, quality and/or degree, like German "so", Polish "tak", and English "such" (mqd demonstratives). These demonstratives modify (some or all of) verbal, nominal and degree expressions. They can be used deictically and anaphorically, and may also occur as correlatives in equative comparison constructions. The example in (1) shows German "so" used deictically.
(1) a. (speaker pointing to someone dancing): So tanzt Anna auch. 'Anna dances like this, too.' -- manner
b. (speaker pointing to a table): So einen Tisch hat Anna auch. 'Anna has such a table / a table like this, too.' -- quality
c. (speaker pointing to a person): So groß ist Anna auch. 'Anna is this tall, too.' -- degree
A semantic interpretation of mqd demonstratives will be proposed starting from the intuition that there is a deictic component and a similarity component involved ¿ in all of (1a-c), the meaning of "so" can be paraphrased by "like this". The basic idea is that mqd demonstratives generate a class of items similar to the target of the pointing gesture, e.g., in (1b) a class of tables similar to the table the speaker points at. This interpretation accounts for fact that mqd demonstratives are directly referential differing from regular demonstratives only in expressing similarity instead of identity. Moreover, it accounts for their cross-categorical distribution.
The suggested analysis is compatible with Carlson's (1980) interpretation of English "such" as a kind- referring expression. In the case of quality and of manner similarity classes will be shown to behave like kinds, although they need not be previously given but are instead ad-hoc generated. In the case of degree, however, it will be argued (contra Anderson and Morzycki 2015) that the resulting similarity class does not establish a kind. In (1c) for example, the class of persons similar in height to the one pointed at does not exhibit kind-like behavior.
The similarity interpretation of mqd demonstratives includes three major research topics:
(i) the implementation of the similarity relation, which is done with the help of multidimensional
(ii) the ad-hoc generation of kinds by similarity, which is shown experimentally to be restricted to
particular features of comparison, and
(iii) the interpretation of equative comparison constructions based on similarity classes.
In the talk, the focus will be on the second topic.
Anderson, C., and M. Morzycki (2015) Degrees as kinds. Natural Language and Linguistic Theory.
Carlson, G. (1980) Reference to kinds in English. New York and London: Garland.
Gust, H. & C.Umbach (2015) Making use of similarity in referential semantics. In H. Christiansen, I. Stojanovic,
G. Papadopoulos (eds.) 9th Conference on Modeling and Using Context, Context 2015, LNCS Springer. Umbach, C., & H. Gust (2014) Similarity Demonstratives. Lingua 149, 74-93.
Joint work with John D. Kelleher, Dublin Institute of Technology, Ireland
Type Theory with Records (TTR) has been proposed as a formal representational framework and a semantic model for embodied agents participating in situated dialogues (Dobnik et al., 2014). Although TTR has many potential advantages as a semantic model for embodied agents, one problem it faces is the combinatorial explosion of types that is implicit in the framework due to the fact that new types can be created dynamically by composing existing types.
A consequence of this combinatorial explosion is that the agent is left with an intractable problem of deciding which types to assign to perceptual data. The term judgement is the technical term used in TTR to describe the assignment of a type to perceptual data that in practice would be implemented as a sensory classification.
This paper makes 3 contributions to the discussion on the applicability of TTR to embodied agents. First, it highlights the problem of the combinatorial explosion of type assignment in TTR. Second, it presents a judgement control mechanism, based on the Load Theory of selective attention and cognitive control (Lavie et al., 2004), that addresses this problem. Third, it presents a computational framework, based on POMDPs (Kaelbling et al., 1998), that offers a basis for future practical experimentation on the feasibility of the proposed approach.
Simon Dobnik is Senior Lecturer at the University of Gothenburg
Probabilistic grammars are an important model family in natural language processing. They are used in the modeling of many problems, mostly prominently in syntax and semantics. Latent-variable grammars are an extension of vanilla probabilistic grammars, introducing latent variables that inject additional information into the grammar by using learning algorithms in the incomplete data setting.
In this talk, I will discuss work aimed at the development of (four) theoretically-motivated algorithms for the estimation of latent-variable grammars. I will discuss how we applied them to syntactic parsing, and more semantically-oriented problems such as machine translation, conversation modeling in online forums and question answering.
Shay Cohen is a Chancellor's Fellow at the Institute for Language, Cognition and Computation, University of Edinburgh.
In this talk, after briefly introducing the formal semantics in modern type theories (MTT-semantics), I shall argue that it is both model-theoretic and proof-theoretic. This is due to the unique features of MTTs: they contain rich type structures that provide powerful representational means (e.g., to represent collections as types) and, at the same time, are specified proof-theoretically as rule-based systems whose sentences (judgements) can be understood inferentially.
Considered in this way, MTTs arguably have promising advantages when employed as foundational languages for formal semantics, both theoretically and practically.
Zhaohui Luo is a Professor of Computer Science at Royal Holloway, University of London.
There is a fair amount of evidence indicating that language acquisition in general crucially relies on probabilistic learning. It is not clear how a reasonable account of semantic learning could be constructed on the basis of the categorical type systems that either classical or revised semantic theories assume. We present probabilistic TTR (Cooper et al 2014) that makes explicit the assumption, common to most probability theories used in AI, that probability is distributed over situation types, rather than over sets of worlds. Improving on and going beyond Cooper et al (2014), we formulate elementary Bayesian classifiers (which can be modelled as two-layer Bayesian networks) in probabilistic TTR and use these to illustrate how our type theory serves as an interface between perceptual judgement, semantic interpretation, and semantic leaning. We also show how this account can be extended to cover general Bayesian nets.
Staffan Larsson is a professor of computational linguistics at CLASP.
Writer-based and reader-based views of text-meaning are reflected by the respective questions "What is the author trying to tell me?" and "What does this text mean to me personally?" Contemporary computational linguistics, however, generally takes neither view; applications do not attempt to answer either question.
Instead, a text is regarded as an object that is independent of, or detached from, its author or provenance, and as an object that has the same meaning for all readers. This is not adequate, however, for the further development of sophisticated NLP applications for intelligence gathering and question answering, let alone interactive dialog systems.
I will review the history of text-meaning in computational linguistics, discuss different views of text-meaning from the perspective of the needs of computational text analysis, and then extend the analysis to include discourse as well -in particular, the collaborative or negotiated construction of meaning and repair of misunderstanding.
Graeme Hirst's research interests cover a range of topics in applied computational linguistics and natural language processing, including lexical semantics, the resolution of ambiguity in text, the analysis of authors' styles in literature and other text (including plagiarism detection and the detection of online sexual predators), identifying markers of Alzheimer's disease in language, and the automatic analysis of arguments and discourse (especially in political and parliamentary texts).
In the last number of years deep learning models have made a significant impact across a range of fields. Machine Translation is one such area of research. The development of the encoder-decoder architecture and its extension to include an attention mechanism has led to deep learning models achieving state of the art MT results for a number of langauge pairs.
However, an open question in deep learning for MT is what is the best attention mechanism to use. This talk will begin by reviewing the current state of the art in deep learning for MT. The second half of the talk will present a novel attention based encoder-decoder architecture for MT. This novel architecture is the result of collaborative research between John Kelleher, Giancarlo Salton, and Robert J. Ross.
John Kelleher is a lecturer in the School of Computing at the Dublin Institute of Technology and a researcher at the Adapt research center. He currently supervises research projects in a number of areas including machine translation, activity recognition and discovery, dialogue systems, computational models of spatial language, and music transcription.
For the last number of years the majority of his research has used a machine learning methodology, and in 2015 he published a textbook on machine learning with MIT Press. John's collaborators on this research are Giancarlo Salton, who is a PhD student at the Dublin Institute of Technology, and Robert Ross who is a senior lecturer in the School of Computing at the Dublin Institute of Technology.
In this talk, I will discuss the use of Modern Type Theoretical Semantics
(MTTs) , i.e. type theories within the tradition of Martin Löf (1974, 1981), for reasoning about natural language semantics. I will first present a brief introduction of the features that make MTTs an attractive formal language to interpret NL semantics to. In particular, I will discuss a number of issues that have been successfully dealt with using MTTs like adjectival/adverbial modification, copredication and intensionality among other things.
Then, I will argue that the proof-theoretic nature of MTTs, i.e. the fact that they are proof-theoretically specified, in combination with their expresiveness makes them fit to perform reasoning tasks. This proof-theoretic aspect of MTTs has been the main reason that a number of proof-assistants implement variants of MTTs. One such proof-assistant, Coq, will be used as a way to show the applicability of MTTs in dealing with Natural Language Inference (NLI).
Firstly, I will show how NL semantics can be implemented in Coq and
then I will present how one can use Coq in order to reason with these
semantics. I will draw examples from the FraCas test suite platform in order to show the predictions the implemented semantics make as regards inference. I will then discuss issues like coverage and proof-automation and a number of ideas for future work, like extracting type ontologies from GWAP lexical networks and creating a parser/translator that will translate between English (or any other language) and the syntax of Coq.
I will end the talk by discussing the potential use of Coq implementing other semantic frameworks, like Montague Semantics, Davidsonian semantics and eventually a discussion on how Coq can be used with TTR (or even ProbTTR).
Stergios Chatzikyriakidis is a researcher and research coordinator of CLASP.
Jan van Eijck, CWI and ILLC, Amsterdam (http://homepages.cwi.nl/~jve/)
(joint work with Fengkui Ju, Beijing Normal University, Beijing, China)
We use propositional dynamic logic and ideas about propositional control
from the agency literature to construct a simple model of how legal
relations interact with actions that change the world, and with actions
that change the legal relations.
This work is relevant for attempts to construct restricted fragments of
natural language for legal reasoning that could be used in the creation of
(more) formal versions of legal documents suitable for `legal knowledge bases'.
In this talk, I will present R language (or simply R ), a dynamic, lazy, functional, programming language that was designed in 1993 by Ross Ihaka and Robert Gentleman. R adopts the underlying evaluation model of Scheme with the syntax of S, (a programming language, which was developed by John Chambers at Bell Laboratories).
R is an open-source programming language and the flexible statistical analysis toolkit implemented in R , made it the lingua franca for doing statistics. The R package repository (CRAN) features 7861 available packages, which extent the language. Also, there are guides on CRAN that group sets of R packages and functions by type of analysis, fields, or methodologies (e.g. Bayesian Inference, Probability Distributions, Machine Learning, Natural Language Processing).
The statistical capabilities of R along with its functional capabilities can transform R into a rich environment for doing Type Theory. Thus, I will conclude this talk by discussing possible extensions of R for A Probabilistic Rich Type Theory for Semantic Interpretation (Cooper, Dobnik, Lappin, and Larsson, 2015).
Charalambos Themistocleous is a post-doc at CLASP.