| 1. |
- Kågebäck, Mikael, 1981, et al.
(författare)
-
Neural context embeddings for automatic discovery of word senses
- 2015
-
Ingår i: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing. Denver, United States. - 9781941643464 ; , s. 25-32
-
Konferensbidrag (refereegranskat)abstract
- Word sense induction (WSI) is the problem of automatically building an inventory of senses for a set of target words using only a text corpus. We introduce a new method for embedding word instances and their context, for use in WSI. The method, Instance-context embedding (ICE), leverages neural word embeddings, and the correlation statistics they capture, to compute high quality embeddings of word contexts. In WSI, these context embeddings are clustered to find the word senses present in the text. ICE is based on a novel method for combining word embeddings using continuous Skip-gram, based on both se- mantic and a temporal aspects of context words. ICE is evaluated both in a new system, and in an extension to a previous system for WSI. In both cases, we surpass previous state-of-the-art, on the WSI task of SemEval-2013, which highlights the generality of ICE. Our proposed system achieves a 33% relative improvement.
|
|
| 2. |
- Mogren, Olof, 1980, et al.
(författare)
-
Extractive summarization by aggregating multiple similarities
- 2015
-
Ingår i: International Conference Recent Advances in Natural Language Processing, RANLP. - 1313-8502. ; 2015, s. 451-457
-
Konferensbidrag (refereegranskat)abstract
- News reports, social media streams, blogs, digitized archives and books are part of a plethora of reading sources that people face every day. This raises the question of how to best generate automatic summaries. Many existing methods for extracting summaries rely on comparing the similarity of two sentences in some way. We present new ways of measuring this similarity, based on sentiment analysis and continuous vector space representations, and show that combining these together with similarity measures from existing methods, helps to create better summaries. The finding is demonstrated with MULTSUM, a novel summarization method that uses ideas from kernel methods to combine sentence similarity measures. Submodular optimization is then used to produce summaries that take several different similarity measures into account. Our method improves over the state-of-the-art on standard benchmark datasets; it is also fast and scale to large document collections, and the results are statistically significant.
|
|
| 3. |
- Tahmasebi, Nina, 1982, et al.
(författare)
-
Visions and open challenges for a knowledge-based culturomics
- 2015
-
Ingår i: International Journal on Digital Libraries. - : Springer Science and Business Media LLC. - 1432-5012 .- 1432-1300. ; 15:2-4, s. 169-187
-
Tidskriftsartikel (refereegranskat)abstract
- The concept of culturomics was born out of the availability of massive amounts of textual data and the interest to make sense of cultural and language phenomena over time. Thus far however, culturomics has only made use of, and shown the great potential of, statistical methods. In this paper, we present a vision for a knowledge-based culturomics that complements traditional culturomics. We discuss the possibilities and challenges of combining knowledge-based methods with statistical methods and address major challenges that arise due to the nature of the data; diversity of sources, changes in language over time as well as temporal dynamics of information in general. We address all layers needed for knowledge-based culturomics, from natural language processing and relations to summaries and opinions.
|
|
