| 1. |
- Andersdotter, Amelia, et al.
(author)
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Godtyckligt regelverk hotar friheten på nätet
- 2013
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In: Dagens Nyheter. - 1101-2447. ; :2013-09-03
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Journal article (pop. science, debate, etc.)abstract
- Reglerna som möjliggör stängning av hemsidor på internet präglas av godtycke och otydlighet. Men det behöver inte vara särskilt svårt att skapa ett nytt och rättssäkert regelverk. Här har Sveriges EU-kommissionär Cecilia Malmström en viktig roll. Frågan är om hon tar sitt ansvar, skriver politiker och nätdebattörer.
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| 2. |
- Andersdotter, Amelia, et al.
(author)
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Utfästelser räcker inte
- 2013
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In: Dagens nyheter (DN debatt). - 1101-2447.
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Journal article (pop. science, debate, etc.)abstract
- Det är nedslående att Cecilia Malmström givit upp kampen när det gäller fri- och rättigheter på nätet, skriver politiker och nätdebattörer. Den rättssäkerhet vi efterfrågar är en sådan som skyddar yttrandefriheten.
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| 3. |
- Forner, Pamela, et al.
(author)
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PROMISE Technology Transfer Day: Spreading the Word on Information Access Evaluation at an Industrial Event : WORKSHOP REPORT
- 2013
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In: SIGIR Forum. - 0163-5840 .- 1558-0229. ; 47:1, s. 53-58
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Journal article (peer-reviewed)abstract
- The Technology Transfer Day was held at CeBIT 2013 from March 5 to March 9, at the Deutsche Messe in Hannover, Germany. PROMISE presented three events at CeBIT: a panel in the CeBIT Global Conference (CGC) - Power Stage, a one-day workshop hosted in the CeBIT Convention Center, and a stand "EU Language & Big Data Projects" in Hall 9. The whole program included 4 panelists, 12 invited talks, and an discussions among the speakers and with the public. This report overviews the aims and contents of the events and outlines the major outcomes.
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| 4. |
- Henriksson, Aron, 1985-
(author)
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Semantic Spaces of Clinical Text : Leveraging Distributional Semantics for Natural Language Processing of Electronic Health Records
- 2013
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Licentiate thesis (other academic/artistic)abstract
- The large amounts of clinical data generated by electronic health record systems are an underutilized resource, which, if tapped, has enormous potential to improve health care. Since the majority of this data is in the form of unstructured text, which is challenging to analyze computationally, there is a need for sophisticated clinical language processing methods. Unsupervised methods that exploit statistical properties of the data are particularly valuable due to the limited availability of annotated corpora in the clinical domain.Information extraction and natural language processing systems need to incorporate some knowledge of semantics. One approach exploits the distributional properties of language – more specifically, term co-occurrence information – to model the relative meaning of terms in high-dimensional vector space. Such methods have been used with success in a number of general language processing tasks; however, their application in the clinical domain has previously only been explored to a limited extent. By applying models of distributional semantics to clinical text, semantic spaces can be constructed in a completely unsupervised fashion. Semantic spaces of clinical text can then be utilized in a number of medically relevant applications.The application of distributional semantics in the clinical domain is here demonstrated in three use cases: (1) synonym extraction of medical terms, (2) assignment of diagnosis codes and (3) identification of adverse drug reactions. To apply distributional semantics effectively to a wide range of both general and, in particular, clinical language processing tasks, certain limitations or challenges need to be addressed, such as how to model the meaning of multiword terms and account for the function of negation: a simple means of incorporating paraphrasing and negation in a distributional semantic framework is here proposed and evaluated. The notion of ensembles of semantic spaces is also introduced; these are shown to outperform the use of a single semantic space on the synonym extraction task. This idea allows different models of distributional semantics, with different parameter configurations and induced from different corpora, to be combined. This is not least important in the clinical domain, as it allows potentially limited amounts of clinical data to be supplemented with data from other, more readily available sources. The importance of configuring the dimensionality of semantic spaces, particularly when – as is typically the case in the clinical domain – the vocabulary grows large, is also demonstrated.
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| 5. |
- Kamps, Jaap, et al.
(author)
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Report on the Fifth Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR’12) : CIKM WORKSHOP REPORT
- 2013
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In: SIGIR Forum. - : Association for Computing Machinery (ACM). - 0163-5840 .- 1558-0229. ; 47:1, s. 38-45
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Journal article (peer-reviewed)abstract
- There is an increasing amount of structure on the web as a result of modern web lan- guages, user tagging and annotation, emerging robust NLP tools, and an ever growing volume of linked data. These meaningful, semantic, annotations hold the promise to significantly en- hance information access, by enhancing the depth of analysis of today’s systems. Currently, we have only started exploring the possibilities and only begin to understand how these valu- able semantic cues can be put to fruitful use. To complicate matters, standard text search excels at shallow information needs expressed by short keyword queries, and here semantic annotation contributes very little, if anything. The main questions for the workshop are how to leverage the rich context currently available, especially in a mobile search scenario, giving powerful new handles to exploit semantic annotations. And how can we fruitfully combine information retrieval and knowledge intensive approaches, and for the first time work actively toward a unified view on exploiting semantic annotations.There was a strong feeling that we made substantial progress. Specifically, each of the breakout groups contributed to our understanding of the way forward. First, there is a need for further integration of symbolic and statistical methods with each adopting parts of the other’s strengths, by focusing on types of annotations that are informed by and meaningful for the task at hand, and relying on automatic information extraction and annotation based on web scale observations. Second, the discussion contributed to the creation of a concrete shared corpus with state of the art semantic annotation—in particular a web crawl annotated with Freebase concepts—that will benefit research in this area for years to come.
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| 6. |
- Karlgren, Jussi
(author)
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New Measures to Investigate Term Typology by Distributional Data
- 2013
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In: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013), May 22–24, 2013, Oslo University, Norway. NEALT Proceedings Series 16. - Linköping : Linköping University Electronic Press.
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Conference paper (peer-reviewed)abstract
- This report describes a series of exploratory experiments to establish whether terms of different semantic type can be distinguished in useful ways in a semantic space constructed from distributional data. The hypotheses explored in this paper are that some words are more variant in their distribution than others; that the varying semantic character of words will be reflected in their distribution; and this distributional difference is encoded in current distributional models, but that the information is not accessible through the methods typically used in application of them. This paper proposes some new measures to explore variation encoded in distributional models but not usually put to use in understanding the character of words represented in them. These exploratory findings show that some proposed measures show a wide range of variation across words of various types.
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| 7. |
- Karlgren, Jussi, et al.
(author)
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Semantic Space Models for Profiling Reputation of Corporate Entities
- 2013
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In: CLEF 2013 Evaluation Labs and Workshop. - : CLEF.
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Conference paper (peer-reviewed)abstract
- Gavagai used its commercially available system for the filtering and po-larity tasks in the evaluation campaign for online reputation management systemsat CLEF 2013. The system is built for large scale analysis of streaming text and aspart of the services Gavagai provides, it measures the public attitude visavi targetsof interest. This mechanism — with no adjustment for this specific task — wasused for polarisation and the experiments performed this year was to test a numberof settings for testing how an attitude might be learned from the data rather thangiven by editorial intervention.
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| 8. |
- Murdock, Vanessa, et al.
(author)
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Report on the Workshop onSearch and Exploration of X-Rated Information(SEXI 2013) : WSDM WORKSHOP REPORT
- 2013
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In: SIGIR Forum. - 0163-5840 .- 1558-0229. ; 47:1, s. 31-37
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Journal article (peer-reviewed)abstract
- The Workshop on Search and Exploration of X-Rated Information (SEXI) was presentedfor the rst time at the Conference on Web Search and Data Mining (WSDM) 2013 inRome, Italy. It represents a rst attempt to study adult content from the perspective of theresearch communities in Web Search and Data Mining. To this end, ve short papers werepresented covering dierent research questions in searching and evaluating adult content onthe Web, with two invited talks from experts in adult content from the elds of evolutionarypsychology and media studies. The day ended with a panel that included the two invitedspeakers, and an expert in human tracking on the Web.
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| 9. |
- Murdock, Vanessa, et al.
(author)
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Search and exploration of X-Rated information (SEXI 2013)
- 2013
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In: WSDM 2013. - New York, NY, USA : Association for Computing Machinery. - 9781450318693 ; , s. 795-796
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Conference paper (peer-reviewed)abstract
- Adult content is pervasive on the Web, has been a driving factor in the adoption of the Internet medium. It is responsible for a significant fraction of traffic and revenues, yet rarely attracts attention in research. We propose that the research questions surrounding adult content access behaviors are unique, and we believe interesting and valuable research in this area can be done ethically. The workshop on Search and Exploration of X-Rated Information (SEXI) addresses these issues for information access tasks related to adult content.
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| 10. |
- Täckström, Oscar, 1979-
(author)
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Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision
- 2013
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Doctoral thesis (other academic/artistic)abstract
- Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties.The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings.Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language.
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