Search (2 results, page 1 of 1)

  • × author_ss:"Aletras, N."
  1. Gonzalez-Agirre, A.; Rigau, G.; Agirre, E.; Aletras, N.; Stevenson, M.: Why are these similar? : investigating item similarity types in a large digital library (2016) 0.00 
    0.0030491136 = product of:
  0.006098227 = sum of:
    0.006098227 = product of:
      0.012196454 = sum of:
        0.012196454 = weight(_text_:a in 3013) [ClassicSimilarity], result of:
          0.012196454 = score(doc=3013,freq=26.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.22966442 = fieldWeight in 3013, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3013)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    We introduce a new problem, identifying the type of relation that holds between a pair of similar items in a digital library. Being able to provide a reason why items are similar has applications in recommendation, personalization, and search. We investigate the problem within the context of Europeana, a large digital library containing items related to cultural heritage. A range of types of similarity in this collection were identified. A set of 1,500 pairs of items from the collection were annotated using crowdsourcing. A high intertagger agreement (average 71.5 Pearson correlation) was obtained and demonstrates that the task is well defined. We also present several approaches to automatically identifying the type of similarity. The best system applies linear regression and achieves a mean Pearson correlation of 71.3, close to human performance. The problem formulation and data set described here were used in a public evaluation exercise, the *SEM shared task on Semantic Textual Similarity. The task attracted the participation of 6 teams, who submitted 14 system runs. All annotations, evaluation scripts, and system runs are freely available.
    Type
    a
  2. Aletras, N.; Baldwin, T.; Lau, J.H.; Stevenson, M.: Evaluating topic representations for exploring document collections (2017) 0.00 
    0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 3325) [ClassicSimilarity], result of:
          0.009567685 = score(doc=3325,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 3325, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3325)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Topic models have been shown to be a useful way of representing the content of large document collections, for example, via visualization interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a term list; that is the top-n words with highest conditional probability within the topic. Other topic representations such as textual and image labels also have been proposed. However, there has been no comparison of these alternative representations. In this article, we compare 3 different topic representations in a document retrieval task. Participants were asked to retrieve relevant documents based on predefined queries within a fixed time limit, presenting topics in one of the following modalities: (a) lists of terms, (b) textual phrase labels, and (c) image labels. Results show that textual labels are easier for users to interpret than are term lists and image labels. Moreover, the precision of retrieved documents for textual and image labels is comparable to the precision achieved by representing topics using term lists, demonstrating that labeling methods are an effective alternative topic representation.
    Type
    a

Authors