activity
20192021
most citedPrincipal Word Vectors

2 citations · 2 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL2021

Syntactic Nuclei in Dependency Parsing -- A Multilingual Exploration

Ali Basirat, Joakim Nivre

Standard models for syntactic dependency parsing take words to be the elementary units that enter into dependency relations. In this paper, we investigate whether there are any ben…

cs.CL2020

Cross-lingual Word Embeddings beyond Zero-shot Machine Translation

Shifei Chen, Ali Basirat

We explore the transferability of a multilingual neural machine translation model to unseen languages when the transfer is grounded solely on the cross-lingual word embeddings. Our…

cs.CL2020

An exploration of the encoding of grammatical gender in word embeddings

Hartger Veeman, Ali Basirat

The vector representation of words, known as word embeddings, has opened a new research approach in linguistic studies. These representations can capture different types of informa…

cs.CL2020

Word embedding and neural network on grammatical gender -- A case study of Swedish

Marc Allassonnière-Tang, Ali Basirat

We analyze the information provided by the word embeddings about the grammatical gender in Swedish. We wish that this paper may serve as one of the bridges to connect the methods o…

cs.CL2020

Greedy Transition-Based Dependency Parsing with Discrete and Continuous Supertag Features

Ali Basirat, Joakim Nivre

We study the effect of rich supertag features in greedy transition-based dependency parsing. While previous studies have shown that sparse boolean features representing the 1-best…

cs.CL20202 cited

Principal Word Vectors

Ali Basirat, Christian Hardmeier, Joakim Nivre

We generalize principal component analysis for embedding words into a vector space. The generalization is made in two major levels. The first is to generalize the concept of the co…