12,206 research outputs found
Representation learning for very short texts using weighted word embedding aggregation
Short text messages such as tweets are very noisy and sparse in their use of
vocabulary. Traditional textual representations, such as tf-idf, have
difficulty grasping the semantic meaning of such texts, which is important in
applications such as event detection, opinion mining, news recommendation, etc.
We constructed a method based on semantic word embeddings and frequency
information to arrive at low-dimensional representations for short texts
designed to capture semantic similarity. For this purpose we designed a
weight-based model and a learning procedure based on a novel median-based loss
function. This paper discusses the details of our model and the optimization
methods, together with the experimental results on both Wikipedia and Twitter
data. We find that our method outperforms the baseline approaches in the
experiments, and that it generalizes well on different word embeddings without
retraining. Our method is therefore capable of retaining most of the semantic
information in the text, and is applicable out-of-the-box.Comment: 8 pages, 3 figures, 2 tables, appears in Pattern Recognition Letter
Data-driven Job Search Engine Using Skills and Company Attribute Filters
According to a report online, more than 200 million unique users search for
jobs online every month. This incredibly large and fast growing demand has
enticed software giants such as Google and Facebook to enter this space, which
was previously dominated by companies such as LinkedIn, Indeed and
CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine",
"Google For Jobs" while Facebook released "Facebook Jobs" within their
platform. These current job search engines and platforms allow users to search
for jobs based on general narrow filters such as job title, date posted,
experience level, company and salary. However, they have severely limited
filters relating to skill sets such as C++, Python, and Java and company
related attributes such as employee size, revenue, technographics and
micro-industries. These specialized filters can help applicants and companies
connect at a very personalized, relevant and deeper level. In this paper we
present a framework that provides an end-to-end "Data-driven Jobs Search
Engine". In addition, users can also receive potential contacts of recruiters
and senior positions for connection and networking opportunities. The high
level implementation of the framework is described as follows: 1) Collect job
postings data in the United States, 2) Extract meaningful tokens from the
postings data using ETL pipelines, 3) Normalize the data set to link company
names to their specific company websites, 4) Extract and ranking the skill
sets, 5) Link the company names and websites to their respective company level
attributes with the EVERSTRING Company API, 6) Run user-specific search queries
on the database to identify relevant job postings and 7) Rank the job search
results. This framework offers a highly customizable and highly targeted search
experience for end users.Comment: 8 pages, 10 figures, ICDM 201
The Google Similarity Distance
Words and phrases acquire meaning from the way they are used in society, from
their relative semantics to other words and phrases. For computers the
equivalent of `society' is `database,' and the equivalent of `use' is `way to
search the database.' We present a new theory of similarity between words and
phrases based on information distance and Kolmogorov complexity. To fix
thoughts we use the world-wide-web as database, and Google as search engine.
The method is also applicable to other search engines and databases. This
theory is then applied to construct a method to automatically extract
similarity, the Google similarity distance, of words and phrases from the
world-wide-web using Google page counts. The world-wide-web is the largest
database on earth, and the context information entered by millions of
independent users averages out to provide automatic semantics of useful
quality. We give applications in hierarchical clustering, classification, and
language translation. We give examples to distinguish between colors and
numbers, cluster names of paintings by 17th century Dutch masters and names of
books by English novelists, the ability to understand emergencies, and primes,
and we demonstrate the ability to do a simple automatic English-Spanish
translation. Finally, we use the WordNet database as an objective baseline
against which to judge the performance of our method. We conduct a massive
randomized trial in binary classification using support vector machines to
learn categories based on our Google distance, resulting in an a mean agreement
of 87% with the expert crafted WordNet categories.Comment: 15 pages, 10 figures; changed some text/figures/notation/part of
theorem. Incorporated referees comments. This is the final published version
up to some minor changes in the galley proof
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
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