Many tasks in NLP stand to benefit from robust measures of semantic similarity for units above the level of individual words. Rich semantic resources such as WordNet provide local semantic information at the lexical level. However, effectively combining this information to compute scores for phrases or sentences is an open problem. Our algorithm aggregates local relatedness information via a ra...

Inverted indexes on external storage perform best when accesses are ordered and data is read sequentially, so that seek times are minimized. As a consequence, the various items required to compute Boolean, ranked and phrase queries are often interleaved in the inverted lists. While suitable for query types in which all items are required, this arrangement has the drawback that other query types...

Like the European Commission, many regulators and standard setters worldwide have substantially revised requirements for auditor’s reports on statutory audits of public interest entities. Their objective was to improve report’s information content and, hence, transparency audit. A significant change introduction a key audit matters (KAM) disclosure which increased scope, meaningfulness, individ...

Clustering of text documents enables unsupervised categorization and facilitates browsing and search. Any clustering method has to embed the objects to be clustered in a suitable representational space that provides a measure of (dis)similarity between any pair of objects. While several clustering methods and the associated similarity measures have been proposed in the past for text clustering,...

Measuring the similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization. This survey discusses the existing works on text similarity through partitioning them into three approaches;...

Explicit semantic analysis (ESA) utilizes an immense Wikipedia index matrix in its interpreter part. This part of the analysis multiplies a large matrix by a term vector to produce a high-dimensional concept vector. A similarity measurement between two texts is performed between two concept vectors with numerous dimensions. The cost is expensive in both interpretation and similarity measurement...

If we are to use electronic plagiarism detectors on student work, it would be interesting to know how much similarity should be expected in independently written documents on a similar topic. If our measure is coarse, the answer should be zero, but a finer grained analysis (such as would be needed to detect inadequate paraphrasing) is likely to detect some background noise. How much background ...

This paper proposes a new indicator of text structure, called the lexical cohesion pro le (LCP), which locates segment boundaries in a text. A text segment is a coherent scene; the words in a segment are linked together via lexical cohesion relations. LCP records mutual similarity of words in a sequence of text. The similarity of words, which represents their cohesiveness, is computed using a s...

This paper describes a new approach to document classification based on visual features alone. Text-based retrieval systems perform poorly on noisy text. We have conducted series of experiments using cosine distance as our similarity measure, selecting varying numbers local interest points per page, and varying numbers of nearest neighbour points in the similarity calculations. We have found th...