The automatic ranking of word pairs as per their semantic relatedness and ability to mimic human notions of semantic relatedness has widespread applications. Measures that rely on raw data (distributional measures) and those that use knowledge-rich ontologies both exist. Although extensive studies have been performed to compare ontological measures with human judgment, the distributional measur...

A conversational agent for entertainment and engagement requires the ability to maintain coherent conversations. We describe the use of semantic relatedness to select the next conversational fragment that an agent utters, to maximise dialogue coherence and/or to suggest new directions for a dialogue. We compare our approach, using a specific semantic relatedness metric, to an existing nearest-c...

Recent work has pointed out the difference between the concepts of semantic similarity and semantic relatedness. Importantly, some NLP applications depend on measures of semantic similarity, while others work better with measures of semantic relatedness. It has also been observed that methods of computing similarity measures from text corpora produce word spaces that are biased towards either s...

The exploitation of the semantic relatedness kernel has always been an appealing subject in the context of text retrieval and information management. Typically, in text classification the documents are represented in the vector space using the bag-of-words (BOW) approach. The BOW approach does not take into account the semantic relatedness information. To further improve the text classification...

We present a system designed to compute the semantic relatedness between a pair of GermaNet concepts (word senses). Five different metrics have been implemented. Three of them are information content based and incorporate the Two metrics constitute the application of a Lesk algorithm (Lesk 1986) to artificial conceptual glosses generated from GermaNet. We show that four metrics correlate very w...

The use of Linked Open Data (LOD) has been explored in recommender systems in different ways, primarily through its graphical representation. The graph structure of LOD is utilized to measure inter-resource relatedness via their semantic distance in the graph. The intuition behind this approach is that the more connected resources are to each other, the more related they are. One drawback of th...

We demonstrate the advantage of specializing semantic word embeddings for either similarity or relatedness. We compare two variants of retrofitting and a joint-learning approach, and find that all three yield specialized semantic spaces that capture human intuitions regarding similarity and relatedness better than unspecialized spaces. We also show that using specialized spaces in NLP tasks and...

News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often su...

Mined Semantic Analysis (MSA) is a novel distributional semantics approach which employs data mining techniques. MSA embraces knowledge-driven analysis of natural languages. It uncovers implicit relations between concepts by mining for their associations in target encyclopedic corpora. MSA exploits not only target corpus content but also its knowledge graph (e.g., "See also" link graph of Wikip...

Semantic relatedness is a special form of linguistic distance between words. Evaluating semantic relatedness measures is usually performed by comparison with human judgments. Previous test datasets had been created analytically and were limited in size. We propose a corpus-based system for automatically creating test datasets.1 Experiments with human subjects show that the resulting datasets co...