The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words, individually. Recent publications in the natural language processing arena, more specifically using word embeddings, try to incorporate semantic aspects into their word vector representation by considering the context of words and how they are distributed in a document collection. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II that combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings into a single decoupled system. In short, our approach has three main contributions: (i) unsupervised techniques that fully integrate word embeddings and lexical chains; (ii) a more solid semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. Knowledge-based systems that use natural language text can benefit from our approach to mitigate ambiguous semantic representations provided by traditional statistical approaches. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show that the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.
This data set is a collection of word similarity benchmarks (RG65, MEN3K, Wordsim 353, simlex999, SCWS, yp130, simverb3500) in their original format and converted into a cosine similarity scale.
In addition, we have two Wikpedia Dumps from 2010 (April) and 2018 (January) in which we provide the original format (raw words), converted using the techniques described in the paper (MSSA, MSSA-D and MSSA-NR) (title in this repository), and also the word embeddings models for 300d and 1000d using a word2vec implementation. A readme.txt is provided with more details for each file.
This collection represents various raw data and analysis of cores extracted during the November 2008 mission of R/V Melville in the Santa Barbara Basin., The core included is the jumbo piston core MV0811-14JC. Core photos, physical properties and magnetic susceptibility from the multisensor track (MST), and the scanning X-ray fluorescence (XRF) data are included in the collection., and Cruise DOI: 10.7284/903459
The research is funded by NSF OCE-1304327.
This data and scripts are meant to test and show seizure differentiation based on bifurcation theory. A zip file is included which contains real and simulated seizure waveforms, Matlab scripts, and metadata. The matlab scripts allow for visual review validation and objective feature analysis. The file “README.txt” provides more detail about each individual file within the zip file. and Data citation: Crisp, D.N., Saggio, M.L., Scott, J., Stacey, W.C., Nakatani, M., Gliske, S.F., Lin, J. (2019). Epidynamics: Navigating the map of seizure dynamics - Code & Data [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/ejhy-5h41
This collection represents various raw data and analysis of cores extracted during the January 2009 mission of the research vessel Sproul in the Santa Barbara Basin., Cores included: box core SPR0901-04BC, box core SPR0901-unnamed, and Kasten core SPR0901-03KC. Core photos, physical properties and magnetic susceptibility from the multisensor track (MST), and the scanning X-ray fluorescence (XRF) data are included in the collection., and Cruise DOI: 10.7284/901089
This research is funded by NSF-OCE 0752093.
This is the flora-fauna lexical material obtained in the course of more general lexical and grammatical fieldwork on languages of central-eastern Mali (Dogon, Songhay, Bangime, Bozo). The spreadsheets in this work, duplicated in xlsx and csv formants, present our flora-fauna lexicons as of early 2019 for many languages of central-eastern Mali, and certain languages of southwestern Burkina Faso. The Malian data is in two spreadsheets (flora, fauna), while the Burkina data is in separate spreadsheets for flora, birds, fish, insects, lizards and snakes, and mammals. Please begin with the “readme” document.
Our project, mainly on Dogon languages of Mali, has branched out to Burkina Faso with emphasis on documentation of the most endangered languages. Tiefo-N was studied on an emergency basis since it was down to two aging competent speakers. For additional comments and links to a reference grammar, see the readme file.
The work on the Bangime language, spoken by the Bangande people, was carried out as part of a larger linguistic fieldwork project focused on Dogon languages. Bangime is confirmed as a language isolate with no demonstrable linguistic relatives—possibly the only such isolate in West Africa.
Jalkunan is an endangered language of the Mande family, spoken in the village cluster of Blédougou in southwestern Burkina Faso. The lexical work complements a published grammar with texts. See the readme for further information.