Generating labeled datasets for schema matching

Matching schemas is a fundamental task in data integration and semantic web applications. However, generating labeled data for schema matching tasks is challenging, requiring an efficient and effective approach. This thesis addresses this challenge by investigating schema matching techniques and crowdsourcing solutions. We developed a prototype...

Automatic Psychological Text Analysis using Support Vector Machine Classification

In recent years, there has been an increasing number of patients with mental disorders. A conversational agent is being developed to ensure an easier diagnosis based on the chat between a patient and the agent. The objective of this research is to assess how well Support Vector Machine (SVM) classifies text into its corresponding schema, which...

Extracting location context from transcripts: a comparison of ELMo and TF-IDF

Using transcripts of the TV-series FRIENDS, this paper explores the problem of predicting the location in which a sentence was said. The research focuses on using feature extraction on the sentences, and training a logistic regression model on those features. Specifically looking at the differences in performance between using ELMo and TF-IDF...

Is Wikipedia succeeding in reducing gender bias?: Assessing the development of gender bias in word embeddings from Wikipedia

Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain a stereotypical gender bias. This unwanted bias is then also present in the word embeddings and in downstream applications in the field of natural language processing. To prevent and reduce this, more knowledge about the gender bias is...

Gender bias in word embeddings of different languages

Word embeddings are useful for various applications, such as sentiment classification (Tang et al., 2014), word translation (Xing, Wang, Liu, & Lin, 2015) and résumé parsing (Nasser, Sreejith, & Irshad, 2018). Previous research has determined that word embeddings contain gender bias, which can be problematic in certain applications such...

Word Embedding Models for Query Expansion in Answer Passage Retrieval

With the increasing popularity of mobile and voice-assisted, extracting short and precise answer passages to open-domain questions is becoming an increasingly important information retrieval (IR) task. The recently released large-scale corpus for answer passage retrieval—WikiPassageQA—was shown to be challenging for both traditional retrieval...

ST-Sem: A Multimodal Method for Points-of-Interest Classification Using Street-Level Imagery

Street-level imagery contains a variety of visual information about the facades of Points of Interest (POIs). In addition to general mor- phological features, signs on the facades of, primarily, business-related POIs could be a valuable source of information about the type and iden- tity of a POI. Recent advancements in computer vision could...

Recognizing and Handling Negations in Machine Learning

In several machine learning problems, a relatively small subproblem is present in which combinations of (negating) objects or structures result in a negation or otherwise other classification compared to when these (negating) objects are not present. To be more specific, a variant of the XOR problem is present in a small amount of objects in...

Semantic Query Processing: Estimating Relational Purity

The use of semantic information found in structured knowledge bases has become an integral part of the processing pipeline of modern intelligent in-<br/>formation systems. However, such semantic information is frequently insuffi-cient to capture the rich semantics demanded by the applications, and thus cor-pus-based methods employing natural...