W oter an Att eeldt Wouter van Atteveldt http://vanatteveldt.com http://content-analysis.org Semantic Network Analysis is a technique in which the content of a message (such as a newspaper article) is extracted from text and represented as a network of semantic relations between actors and issues, which can be queried to look for specific patterns and answer various research questions. This technique yields data that are easier to compare, combine, and share than traditional content analysis techniques.

http://vanatteveldt.com/wp-content/uploads/vanatteveldt_semanticnetworkanalysis.pdf

Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content

How can search engines use the hyperlinks between documents to determine which documents are the most relevant for a search query? Some search engines use links to determine popularity, where the underlying idea is that the number of links pointing to a document (Web page) is a measure of its popularity. Another aspect of links is they provide a signal that two documents have related content. After all, a link is a reference. If a document A is relevant for a search query, then documents linked to A are possibly also relevant. Link information could possibly contain evidence for the topical relevance of a document.The value of link evidence for ir was investigated through the trec Web Tracks of 1999--2004. First through the traditional ad hoc search methodology, with disappointing results. When ad hoc search was considered an inappropriate model for Web search, attention moved to moreWeb-centric search tasks such as navigational search, with great success. Home pages and other importantWeb pages tend to be pages with many incoming links, and link evidence such as indegree counts and PageRank proved highly effective.The question of why links are not useful for measuring topical relevance was never answered. The goal of this thesis is to give a more precise and complete account of the value of link evidence for information retrieval. This is first investigated using the English Wikipedia, because it is available in its entirety, including all the links, and comes with a large, highquality test collection in the form of the inex Ad Hoc collection of 2006--2007. As an encyclopedia, Wikipedia is a natural collection to study topical relevance search tasks.Link information can be derived from the link graph of the entire collection--giving global, query-independent information--or from a subset of the link graph derived from the top results for a given query produced by a text-based retrieval system, giving local, queryindependent link information. PageRank is often computed on the global link graph, while algorithms like hits are typically used on the local links of the top-ranked results.We find that for ad hoc search on the inex 2006 Wikipedia collection, local link evidence leads to significant performance improvements. Incoming and outgoing link information is equally effective; the direction of the links does not affect their value for ad hoc search. We compare this to Web-centric search tasks on the trec 2004 Web track collection and to the ad hoc task on the trec 2009 Web track collection. For Web-centric search tasks, such as home page and named page finding, incoming link evidence is more effective than outgoing link evidence, and global link evidence is more effective than local link evidence. The value of link evidence for Web-centric search is to identify popular and authoritative pages, and the link direction determines the value. For ad hoc search on the more recent ClueWeb09 collection, global outgoing link evidence is very effective, but turns out to favour Wikipedia pages, which are densely interlinked. If we remove Wikipedia from the ClueWeb09 collection, local link evidence is more effective than global link evidence, and incoming and outgoing link evidence are equally effective. We also find that site-internal links are more effective for ad hoc search than site-external links. To determine the impact of link density, we conduct filtering experiments, which show that link density has a minimal impact on the effectiveness of global link evidence. For local link evidence, removing links gradually reduces the impact of link evidence, but even with a small number of links, link evidence is still effective. We also experiment with filtering links based on the semantic relatedness of the link documents and find that links between semantically related pages are more effective for identifying topically relevant documents than links between unrelated pages.Further experiments on the inex Wikipedia collection show that the amount of local link evidence is related to the amount of relevant text in documents, and the fraction of local link evidence (the percentage of the global links incident to a document that are present in the local link graph) is related to the fraction of text in a document that is relevant.With these findings, the value of link information for ranking search results has become clearer and more complete. Link information can be evidence for both popularity and topical relevance. The meaning of information derived from the link structure is determined by the direction of the links, the topical relation between the linked documents and the selection of links that is used as evidence.Available at http://staff.science.uva.nl/~mhakoole/2011/kool:mean11.pdf.

/pdf/the-meaning-of-structure-the-value-of-link-evidence-for-s3jfrgy8sj.pdf

The meaning of structure: the value of link evidence for information retrieval

In this thesis, research on large vocabulary continuous speech recognition for unknown audio conditions is presented. For automatic speech recognition systems based on statistical methods, it is important that the conditions of the audio used for training the statistical models match the conditions of the audio to be processed. Any mismatch will decrease the accuracy of the recognition. If it is unpredictable what kind of data can be expected, or in other words if the conditions of the audio to be processed are unknown, it is impossible to tune the models. If the material consists of `surprise data' the output of the system is likely to be poor. In this thesis methods are presented for which no external training data is required for training models. These novel methods have been implemented in a large vocabulary continuous speech recognition system called SHoUT. This system consists of three subsystems: speech/non-speech classification, speaker diarization and automatic speech recognition.

/pdf/segmentation-diarization-and-speech-transcription-surprise-3e76w1pz6d.pdf

Segmentation, Diarization and Speech Transcription: Surprise Data Unraveled

htmlabstractWith the performance of modern computers improving at a rapid pace, database 
technology has problems with fully exploiting the benefits that each new hardware 
generation brings. This has caused a significant performance gap between 
general-purpose databases and specialized, application-optimized solutions for 
large-volume computation-intensive processing problems, as found in areas including 
information retrieval, scientific data management and decision support. 
This thesis attempts to enhance the state-of-the-art in architecture-conscious 
database research, both in the query execution layer as well as in the data storage 
layer, and in the way these work together. Thus, rather than focusing on 
an isolated problem or algorithm, the thesis presents a new database system 
architecture, realized in the MonetDB/X100 prototype, that combines a coherent 
set of new architecture-conscious techniques that are designed to work well 
together. 
The motivation for the new query execution layer comes from the analysis 
of the problems of two popular approaches to query processing: tuple-at-a-time 
operator pipelining, used in most existing systems, and column-at-a-time materializing 
operators, found in MonetDB. MonetDB/X100 proposes a new vectorized 
in-cache execution model, that exploits ideas from both approaches, and combines 
the scalability of the former with the high-performance bulk processing of 
the latter. This is achieved by modifying the traditional operator pipeline model 
to operate on cache-resident vectors of data using highly optimized primitive 
functions. Additionally, within this architecture, a set of hardware-conscious 
design and programming techniques is presented, enabling efficient execution 
of typical data processing tasks. The resulting query execution layer efficiently 
exploits modern super-scalar CPUs and cache-memory systems and achieves 
in-memory performance often one or two orders of magnitude higher than the 
existing approaches. 
In the storage area there are two hardware trends that significantly influence 
219 
220 Summary 
database performance. First, the imbalance between sequential disk bandwidth 
and random disk latency continuously increases. As a result, access methods that 
rely on random I/O become less attractive, making various forms of sequential 
access the preferred option. MonetDB/X100 follows this idea with ColumnBM 
– a bandwidth-optimized column store. Secondly, both disk bandwidth and latency 
improve significantly slower than the computing power of modern CPUs, 
especially with the advent of multi-core CPUs. ColumnBM introduces two techniques 
that address this issue. Lightweight in-cache compression allows trading 
some processor time for an increased perceived disk bandwidth. High decompression 
performance is achieved by applying the decompression on the RAMcache 
boundary, providing cache-resident data directly to the execution layer. 
Additionally, introduced family of compression methods provides performance 
an order of magnitude higher than previous solutions. Cooperative scans observe 
current system activity and dynamically schedule I/O operations to exploit overlapping 
demands of different queries. This allows to amortize the cost of disk 
access among multiple consumers, and also better utilize the available buffer 
space, providing much better performance with many concurrently executing 
queries. 
By combining the CPU-efficient processing with a bandwidth-optimized storage 
facility, MonetDB/X100 has been able to achieve its high in-memory raw 
query execution power also on huge disk-resident datasets. We evaluated its 
performance both on TPC-H decision support data sets as well as in the area 
of large-volume information retrieval (the Terabyte TREC task), where it successfully 
competed with the specialized solutions, both for in-memory and diskbased 
tasks.

/pdf/balancing-vectorized-query-execution-with-bandwidth-3bf5a3pf2b.pdf

Balancing vectorized query execution with bandwidth-optimized storage

The amount of digital data we produce every day far surpasses our ability to process this data, and finding useful information in this constant flow of data has become one of the major challenges of the 21st century. Search engines are one way of accessing large data collections. Their algorithms have evolved far beyond simply matching search queries to sets of documents. Today’s most sophisticated search engines combine hundreds of relevance signals to provide the best possible results for each searcher. Current approaches for tuning the parameters of search engines can be highly effective. However, they typically require considerable expertise and manual effort. They rely on supervised learning to rank, meaning that they learn from manually annotated examples of relevant documents for given queries. Obtaining large quantities of sufficiently accurate manual annotations is becoming increasingly difficult, especially for personalized search, access to sensitive data, or search in settings that change over time. In this thesis, I develop new online learning to rank techniques, based on insights from reinforcement learning. In contrast to supervised approaches, these methods allow search engines to learn directly from users’ interactions. User interactions can typically be observed easily and cheaply, and reflect the preferences of real users. Interpreting user interactions and learning from them is challenging, because they can be biased and noisy. The contributions of this thesis include a novel interleaved comparison method, called probabilistic interleave, that allows unbiased comparisons of search engine result rankings, and methods for learning quickly and effectively from the resulting relative feedback. The obtained analytical and experimental results show how search engines can effectively learn from user interactions. In the future, these and similar techniques can open up new ways for gaining useful information from ever larger amounts of data.

/pdf/fast-and-reliable-online-learning-to-rank-for-information-40rh9l5ybt.pdf

Fast and reliable online learning to rank for information retrieval

Text retrieval is an active area of research since decades Several issues have
been studied over the entire period, like the development of statistical models
for the estimation of relevance, or the challenge to keep retrieval tasks efficient with ever growing text collections Especially in the last decade, we have also seen a diversification of retrieval tasks Passage or XML retrieval systems allow a more focused search Question answering or expert search systems
do not even return a ranked list of text units, but for instance persons with expertise on a given topic The sketched situation forms the starting point of this thesis, which presents a number of task-specific search solutions and tries to set them into more generic frameworks In particular, we take a look at the three areas (1) context adaptivity of search, (2) efficient XML retrieval, and (3) entity ranking
In the first case, we show how different types of context information can
be incorporated in the retrieval of documents When users are searching for
information, the search task is typically part of a wider working process This
search context, however, is often not reflected by the few search keywords
stated to the retrieval system, though it can contain valuable information for
query refinement We address with this work two research questions related
to the aim of developing context-aware retrieval systems First, we show
how already available information about the user’s context can be employed
effectively to gain highly precise search results Second, we investigate how
such meta-data about the search context can be gathered The proposed
“query profiles” have a central role in the query refinement process They
automatically detect necessary context information and help the user to explicitly
express context-dependent search constraints The effectiveness of
the approach is tested with retrieval experiments on newspaper data
When documents are not regarded as a simple sequence of words, but their content is structured in a machine readable form, it is attractive to
try to develop retrieval systems that make use of the additional structure
information Structured retrieval first asks for the design of a suitable language
that enables the user to express queries on content and structure We
investigate here existing query languages, whether and how they support
the basic needs of structured querying However, our main focus lies on the
efficiency of structured retrieval systems Conventional inverted indices for
document retrieval systems are not suitable for maintaining structure indices
We identify base operations involved in the execution of structured queries
and show how they can be supported by new indices and algorithms on a
database system Efficient query processing has to be concerned with the
optimization of query plans as well We investigate low-level query plans of
physical database operators for the execution of simple query patterns Furthermore,
It is demonstrated how complex queries benefit from higher level
query optimization
New search tasks and interfaces for the presentation of search results,
like faceted search applications, question answering, expert search, and automatic
timeline construction, come with the need to rank entities instead of
documents By entities we mean unique (named) existences, such as persons,
organizations or dates Modern language processing tools are able to automatically
detect and categorize named entities in large text collections In
order to estimate their relevance to a given search topic, we develop retrieval
models for entities which are based on the relevance of texts that mention the
entity A graph-based relevance propagation framework is introduced for this
purpose that enables to derive the relevance of entities Several options for
the modeling of entity containment graphs and different relevance propagation
approaches are tested, demonstrating the usefulness of the graph-based
ranking framework

/pdf/from-document-to-entity-retrieval-improving-precision-and-1s5dt114x9.pdf

Henning Rode

Papers

From document to entity retrieval : improving precision and performance of focused text search