Search results for "Word2vec"
showing 5 items of 5 documents
CitySearcher: A City Search Engine For Interests
2017
We introduce CitySearcher, a vertical search engine that searches for cities when queried for an interest. Generally in search engines, utilization of semantics between words is favorable for performance improvement. Even though ambiguous query words have multiple semantic meanings, search engines can return diversified results to satisfy different users' information needs. But for CitySearcher, mismatched semantic relationships can lead to extremely unsatisfactory results. For example, the city Sale would incorrectly rank high for the interest shopping because of semantic interpretations of the words. Thus in our system, the main challenge is to eliminate the mismatched semantic relationsh…
A Neural Network model for the Evaluation of Text Complexity in Italian Language: a Representation Point of View
2018
Abstract The goal of a text simplification system (TS) is to create a new text suited to the characteristics of a reader, with the final goal of making it more understandable.The building of an Automatic Text Simplification System (ATS) cannot be separated from a correct evaluation of the text complexity. In fact the ATS must be capable of understanding if a text should be simplified for the target reader or not. In a previous work we have presented a model capable of classifying Italian sentences based on their complexity level. Our model is a Long Short Term Memory (LSTM) Neural Network capable of learning the features of easy-to-read and complex-to-read sentences autonomously from a anno…
Graph-based exploration and clustering analysis of semantic spaces
2019
Abstract The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases. Specifically, we construct semantic networks based on word2vec representation of words, which is “learnt” from large text corpora (Google news, Amazon reviews), and “human built” word networks derived from the well-known lexical databases: WordNet and Moby Thesaurus. We compare “global” (e.g., degrees, distances, clustering coefficients) and “local” (e.g., most central nodes and community-type dense clusters) characteristics of considered networks. Our observations suggest that …
Automātiska teksta konspektēšana izmantojot jēdzientelpu
2016
Šobrīd pasaulē ir vērojams milzīgs informācijas daudzuma pieaugums un ir arvien grūtāk iepazīties ar šo informāciju. Automātiskas teksta konspektēšanas mērķis ir spēt pārveidot lielu tekstuālas informācijas daudzumu īsākā formātā, kurš spēj saglabāt oriģinālā teksta svarīgāko informāciju. Viena no metodēm kā automātiski konspektēt tekstu ir izvēlēties svarīgākos teikumus no teksta. Mērķis ir izvēlēties teikumus tā, lai tajos esošā informācija savstarpēji nepārklājas, kā arī nosedz pietiekamu daļu no konspektējamā teksta. Lai to izdarītu ir jāsalīdzina teikumu ietvertās informācijas līdzīgums. Jēdzientelpa ir moderns rīks, ar kura palīdzību var noteikt vārdu nozīmi un līdzību ar citiem vārdi…
Zero-shot Semantic Segmentation using Relation Network
2021
Zero-shot learning (ZSL) is widely studied in recent years to solve the problem of lacking annotations. Currently, most studies on ZSL are for image classification and object detection. But, zero-shot semantic segmentation, pixel level classification, is still at its early stage. Therefore, this work proposes to extend a zero-shot image classification model, Relation Network (RN), to semantic segmentation tasks. We modified the structure of RN based on other state-of-the-arts semantic segmentation models (i.e. U-Net and DeepLab) and utilizes word embeddings from Caltech-UCSD Birds 200-2011 attributes and natural language processing models (i.e. word2vec and fastText). Because meta-learning …