Predicting sense convergence with distributional semantics: an application to the CogaLex 2014 shared task
نویسندگان
چکیده
This paper presents our system to address the CogALex-IV 2014 shared task of identifying a single word most semantically related to a group of 5 words (queries). Our system uses an implementation of a neural language model and identifies the answer word by finding the most semantically similar word representation to the sum of the query representations. It is a fully unsupervised system which learns on around 20% of the UkWaC corpus. It correctly identifies 85 exact correct targets out of 2,000 queries, 285 approximate targets in lists of 5 suggestions.
منابع مشابه
NaDiR: Naive Distributional Response Generation
This paper describes NaDiR (Naive DIstributional Response generation), a corpus-based system that, from a set of word stimuli as an input, generates a response word relying on association strength and distributional similarity. NaDiR participated in the CogALex 2014 shared task on multiword associations (restricted systems track), operationalizing the task as a ranking problem: candidate words ...
متن کاملCogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations
We present a submission to the CogALex 2016 shared task on the corpus-based identification of semantic relations, using LexNET (Shwartz and Dagan, 2016), an integrated path-based and distributional method for semantic relation classification. The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semant...
متن کاملCogALex-V Shared Task: ROOT18
In this paper, we describe ROOT 18, a classifier using the scores of several unsupervised distributional measures as features to discriminate between semantically related and unrelated words, and then to classify the related pairs according to their semantic relation (i.e. synonymy, antonymy, hypernymy, part-whole meronymy). Our classifier participated in the CogALex-V Shared Task, showing a so...
متن کاملCogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks
In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared task of corpus-based identification of semantic relations. Proposed approach using CNN-based deep neural networks leveraging pre-compiled word2vec distributional neural embeddings ...
متن کاملRegular polysemy: from sense vectors to sense patterns
Regular polysemy was extensively investigated in lexical semantics, but this phenomenon has been very little studied in distributional semantics. We propose a model for regular polysemy detection that is based on sense vectors and allows to work directly with senses in semantic vector space. Our method is able to detect polysemous words that have the same regular sense alternation as in a given...
متن کامل