no code implementations • 24 Jul 2017 • Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lió, Nigel Collier
We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as cross-lingual vector space mapping approaches (Least Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge.
no code implementations • COLING 2016 • Dimitri Kartsaklis, Mehrnoosh Sadrzadeh
According to the distributional inclusion hypothesis, entailment between words can be measured via the feature inclusions of their distributional vectors.
no code implementations • 5 Jun 2016 • Dimitri Kartsaklis
An open problem with categorical compositional distributional semantics is the representation of words that are considered semantically vacuous from a distributional perspective, such as determiners, prepositions, relative pronouns or coordinators.
no code implementations • 14 Dec 2015 • Esma Balkir, Dimitri Kartsaklis, Mehrnoosh Sadrzadeh
In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein.
no code implementations • EMNLP 2015 • Jianpeng Cheng, Dimitri Kartsaklis
We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence.
no code implementations • WS 2015 • Dimitri Kartsaklis, Mehrnoosh Sadrzadeh
The categorical compositional distributional model of Coecke, Sadrzadeh and Clark provides a linguistically motivated procedure for computing the meaning of a sentence as a function of the distributional meaning of the words therein.
no code implementations • 1 May 2015 • Dimitri Kartsaklis
As a last contribution, I formalize the explicit treatment of lexical ambiguity in the context of the categorical framework by resorting to categorical quantum mechanics (joint work with Coecke).
no code implementations • 3 Feb 2015 • Robin Piedeleu, Dimitri Kartsaklis, Bob Coecke, Mehrnoosh Sadrzadeh
Moreover, just like CQM allows for varying the model in which we interpret quantum axioms, one can also vary the model in which we interpret word meaning.
no code implementations • 12 May 2014 • Dimitri Kartsaklis, Mehrnoosh Sadrzadeh
In both quantum mechanics and corpus linguistics based on vector spaces, the notion of entanglement provides a means for the various subsystems to communicate with each other.
no code implementations • 15 Nov 2014 • Jianpeng Cheng, Dimitri Kartsaklis, Edward Grefenstette
This paper aims to explore the effect of prior disambiguation on neural network- based compositional models, with the hope that better semantic representations for text compounds can be produced.
no code implementations • ACL 2014 • Dimitri Kartsaklis, Nal Kalchbrenner, Mehrnoosh Sadrzadeh
This paper provides a method for improving tensor-based compositional distributional models of meaning by the addition of an explicit disambiguation step prior to composition.
no code implementations • EMNLP 2014 • Dmitrijs Milajevs, Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, Matthew Purver
We provide a comparative study between neural word representations and traditional vector spaces based on co-occurrence counts, in a number of compositional tasks.
no code implementations • 23 Jan 2014 • Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, Stephen Pulman, Bob Coecke
They also provide semantics for Lambek's pregroup algebras, applied to formalizing the grammatical structure of natural language, and are implicit in a distributional model of word meaning based on vector spaces.
no code implementations • 21 Jan 2014 • Dimitri Kartsaklis
This survey presents in some detail the main advances that have been recently taking place in Computational Linguistics towards the unification of the two prominent semantic paradigms: the compositional formal semantics view and the distributional models of meaning based on vector spaces.
no code implementations • EMNLP 2018 • Dimitri Kartsaklis, Mohammad Taher Pilehvar, Nigel Collier
Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities.
no code implementations • EMNLP 2018 • Mohammad Taher Pilehvar, Dimitri Kartsaklis, Victor Prokhorov, Nigel Collier
Rare word representation has recently enjoyed a surge of interest, owing to the crucial role that effective handling of infrequent words can play in accurate semantic understanding.
no code implementations • 6 Nov 2018 • Martha Lewis, Bob Coecke, Jules Hedges, Dimitri Kartsaklis, Dan Marsden
Moreover, the interaction between the three disciplines of cognitive science, linguistics and game theory is a fertile ground for research.
no code implementations • 12 Nov 2018 • Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lio, Nigel Collier
Word embedding techniques heavily rely on the abundance of training data for individual words.
no code implementations • COLING 2016 • Mehrnoosh Sadrzadeh, Dimitri Kartsaklis
Compositional distributional models of meaning (CDMs) provide a function that produces a vectorial representation for a phrase or a sentence by composing the vectors of its words.
no code implementations • ACL (SemSpace, IWCS) 2021 • Richie Yeung, Dimitri Kartsaklis
While the DisCoCat model (Coecke et al., 2010) has been proved a valuable tool for studying compositional aspects of language at the level of semantics, its strong dependency on pregroup grammars poses important restrictions: first, it prevents large-scale experimentation due to the absence of a pregroup parser; and second, it limits the expressibility of the model to context-free grammars.
1 code implementation • 27 Nov 2023 • Charles London, Douglas Brown, Wenduan Xu, Sezen Vatansever, Christopher James Langmead, Dimitri Kartsaklis, Stephen Clark, Konstantinos Meichanetzidis
By constructing quantum models based on parameterised quantum circuits we perform sequence classification on a task relevant to the design of therapeutic proteins, and find competitive performance with classical baselines of similar scale.
3 code implementations • 25 Feb 2021 • Robin Lorenz, Anna Pearson, Konstantinos Meichanetzidis, Dimitri Kartsaklis, Bob Coecke
Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing.
1 code implementation • EMNLP 2020 • Jianpeng Cheng, Devang Agrawal, Hector Martinez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Shaona Ghosh, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D Williams, Hong Yu, Diarmuid O Seaghdha, Anders Johannsen
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog.
1 code implementation • 8 Oct 2021 • Dimitri Kartsaklis, Ian Fan, Richie Yeung, Anna Pearson, Robin Lorenz, Alexis Toumi, Giovanni De Felice, Konstantinos Meichanetzidis, Stephen Clark, Bob Coecke
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP).