Search Results for author: Dimitri Kartsaklis

Found 27 papers, 4 papers with code

Peptide Binding Classification on Quantum Computers

1 code implementation27 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.


A CCG-Based Version of the DisCoCat Framework

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.

QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

3 code implementations25 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.

Sentence Sentence Classification

Proceedings of the 2018 Workshop on Compositional Approaches in Physics, NLP, and Social Sciences

no code implementations6 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.

Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models

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.

Word Embeddings Word Similarity

Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs

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.

General Classification Sentence +1

Learning Rare Word Representations using Semantic Bridging

no code implementations24 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.

Graph Embedding Word Similarity

Compositional Distributional Models of Meaning

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.

Machine Translation Natural Language Inference +2

Distributional Inclusion Hypothesis for Tensor-based Composition

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.


Coordination in Categorical Compositional Distributional Semantics

no code implementations5 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.

Sentence Entailment in Compositional Distributional Semantics

no code implementations14 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.


Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning

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.

Sentence Sentence Embeddings +1

A Frobenius Model of Information Structure in Categorical Compositional Distributional Semantics

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.


Compositional Distributional Semantics with Compact Closed Categories and Frobenius Algebras

no code implementations1 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).

Open System Categorical Quantum Semantics in Natural Language Processing

no code implementations3 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.


Investigating the Role of Prior Disambiguation in Deep-learning Compositional Models of Meaning

no code implementations15 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.

Evaluating Neural Word Representations in Tensor-Based Compositional Settings

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.

Sentence Sentence Similarity +1

Resolving Lexical Ambiguity in Tensor Regression Models of Meaning

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.


A Study of Entanglement in a Categorical Framework of Natural Language

no code implementations12 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.

Reasoning about Meaning in Natural Language with Compact Closed Categories and Frobenius Algebras

no code implementations23 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.

Compositional Operators in Distributional Semantics

no code implementations21 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.


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