9,445 research outputs found
UoS: a graph-based system for graded word sense induction
This paper presents UoS, a graph-based Word Sense Induction system which attempts to find all applicable senses of a target word given its context, grading each sense according to its suitability to the context. Senses of a target word are induced through use of a non-parameterised, linear-time clustering algorithm that returns maximal quasi-strongly connected components of a target word graph in which vertex pairs are assigned to the same cluster if either vertex has the highest edge weight to the other. UoS participated in SemEval-2013 Task 13: Word Sense Induction for Graded and Non-Graded Senses. Two system were submitted; both systems returned results comparable with those of the best performing systems
Breaking Sticks and Ambiguities with Adaptive Skip-gram
Recently proposed Skip-gram model is a powerful method for learning
high-dimensional word representations that capture rich semantic relationships
between words. However, Skip-gram as well as most prior work on learning word
representations does not take into account word ambiguity and maintain only
single representation per word. Although a number of Skip-gram modifications
were proposed to overcome this limitation and learn multi-prototype word
representations, they either require a known number of word meanings or learn
them using greedy heuristic approaches. In this paper we propose the Adaptive
Skip-gram model which is a nonparametric Bayesian extension of Skip-gram
capable to automatically learn the required number of representations for all
words at desired semantic resolution. We derive efficient online variational
learning algorithm for the model and empirically demonstrate its efficiency on
word-sense induction task
Unsupervised does not mean uninterpretable : the case for word sense induction and disambiguation
This dataset contains the models for interpretable Word Sense Disambiguation (WSD) that were employed in Panchenko et al. (2017; the paper can be accessed at https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/EACL_Interpretability___FINAL__1_.pdf).
The files were computed on a 2015 dump from the English Wikipedia. Their contents:
Induced Sense Inventories: wp_stanford_sense_inventories.tar.gz
This file contains 3 inventories (coarse, medium fine)
Language Model (3-gram): wiki_text.3.arpa.gz
This file contains all n-grams up to n=3 and can be loaded into an index
Weighted Dependency Features: wp_stanford_lemma_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000.gz
This file contains weighted word--context-feature combinations and includes their count and an LMI significance score
Distributional Thesaurus (DT) of Dependency Features: wp_stanford_lemma_BIM_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000_simsortlimit200_feature expansion.gz
This file contains a DT of context features. The context feature similarities can be used for context expansion
For further information, consult the paper and the companion page: http://jobimtext.org/wsd/
Panchenko A., Ruppert E., Faralli S., Ponzetto S. P., and Biemann C. (2017): Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL'2017). Valencia, Spain. Association for Computational Linguistics
An approach to source-code plagiarism detection investigation using latent semantic analysis
This thesis looks at three aspects of source-code plagiarism. The first aspect of the
thesis is concerned with creating a definition of source-code plagiarism; the second aspect
is concerned with describing the findings gathered from investigating the Latent Semantic
Analysis information retrieval algorithm for source-code similarity detection; and the final
aspect of the thesis is concerned with the proposal and evaluation of a new algorithm that
combines Latent Semantic Analysis with plagiarism detection tools.
A recent review of the literature revealed that there is no commonly agreed definition of
what constitutes source-code plagiarism in the context of student assignments. This thesis
first analyses the findings from a survey carried out to gather an insight into the perspectives
of UK Higher Education academics who teach programming on computing courses. Based
on the survey findings, a detailed definition of source-code plagiarism is proposed.
Secondly, the thesis investigates the application of an information retrieval technique,
Latent Semantic Analysis, to derive semantic information from source-code files. Various
parameters drive the effectiveness of Latent Semantic Analysis. The performance of Latent
Semantic Analysis using various parameter settings and its effectiveness in retrieving
similar source-code files when optimising those parameters are evaluated.
Finally, an algorithm for combining Latent Semantic Analysis with plagiarism detection
tools is proposed and a tool is created and evaluated. The proposed tool, PlaGate, is
a hybrid model that allows for the integration of Latent Semantic Analysis with plagiarism
detection tools in order to enhance plagiarism detection. In addition, PlaGate has a facility
for investigating the importance of source-code fragments with regards to their contribution
towards proving plagiarism. PlaGate provides graphical output that indicates the clusters of
suspicious files and source-code fragments
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