3 research outputs found
Personal information search on mobile devices
Today's mobile devices, especially mobile phones, are comparable in computing capability and storage to the desktop computers of a few years ago. The volume and diversity of the information kept on mobile devices has continually increased and users have taken advantage of this. Since information is being stored on multiple devices, searching for and retrieving the desired information has become an important function. This thesis focuses on search with regard to Personal Information Management (PIM) on mobile devices. A search system which involves different types of mobile devices is also introduced.Turkish Army author
Answering questions about archived, annotated meetings
Retrieving information from archived meetings is a new domain of information retrieval that has received increasing attention in the past few years. Search in spontaneous spoken conversations has been recognized as more difficult than text-based document retrieval because meeting discussions contain two levels of information: the content itself, i.e. what topics are discussed, but also the argumentation process, i.e. what conflicts are resolved and what decisions are made. To capture the richness of information in meetings, current research focuses on recording meetings in Smart-Rooms, transcribing meeting discussion into text and annotating discussion with semantic higher-level structures to allow for efficient access to the data. However, it is not yet clear what type of user interface is best suited for searching and browsing such archived, annotated meetings. Content-based retrieval with keyword search is too naive and does not take into account the semantic annotations on the data. The objective of this thesis is to assess the feasibility and usefulness of a natural language interface to meeting archives that allows users to ask complex questions about meetings and retrieve episodes of meeting discussions based on semantic annotations. The particular issues that we address are: the need of argumentative annotation to answer questions about meetings; the linguistic and domain-specific natural language understanding techniques required to interpret such questions; and the use of visual overviews of meeting annotations to guide users in formulating questions. To meet the outlined objectives, we have annotated meetings with argumentative structure and built a prototype of a natural language understanding engine that interprets questions based on those annotations. Further, we have performed two sets of user experiments to study what questions users ask when faced with a natural language interface to annotated meeting archives. For this, we used a simulation method called Wizard of Oz, to enable users to express questions in their own terms without being influenced by limitations in speech recognition technology. Our experimental results show that technically it is feasible to annotate meetings and implement a deep-linguistic NLU engine for questions about meetings, but in practice users do not consistently take advantage of these features. Instead they often search for keywords in meetings. When visual overviews of the available annotations are provided, users refer to those annotations in their questions, but the complexity of questions remains simple. Users search with a breadth-first approach, asking questions in sequence instead of a single complex question. We conclude that natural language interfaces to meeting archives are useful, but that more experimental work is needed to find ways to incent users to take advantage of the expressive power of natural language when asking questions about meetings
Toponym Resolution in Text
Institute for Communicating and Collaborative SystemsBackground. In the area of Geographic Information Systems (GIS), a shared discipline between
informatics and geography, the term geo-parsing is used to describe the process of identifying
names in text, which in computational linguistics is known as named entity recognition
and classification (NERC). The term geo-coding is used for the task of mapping from implicitly
geo-referenced datasets (such as structured address records) to explicitly geo-referenced
representations (e.g., using latitude and longitude). However, present-day GIS systems provide
no automatic geo-coding functionality for unstructured text.
In Information Extraction (IE), processing of named entities in text has traditionally been seen
as a two-step process comprising a flat text span recognition sub-task and an atomic classification
sub-task; relating the text span to a model of the world has been ignored by evaluations
such as MUC or ACE (Chinchor (1998); U.S. NIST (2003)).
However, spatial and temporal expressions refer to events in space-time, and the grounding of
events is a precondition for accurate reasoning. Thus, automatic grounding can improve many
applications such as automatic map drawing (e.g. for choosing a focus) and question answering
(e.g. , for questions like How far is London from Edinburgh?, given a story in which both occur
and can be resolved). Whereas temporal grounding has received considerable attention in the
recent past (Mani and Wilson (2000); Setzer (2001)), robust spatial grounding has long been
neglected.
Concentrating on geographic names for populated places, I define the task of automatic
Toponym Resolution (TR) as computing the mapping from occurrences of names for places as
found in a text to a representation of the extensional semantics of the location referred to (its
referent), such as a geographic latitude/longitude footprint.
The task of mapping from names to locations is hard due to insufficient and noisy databases,
and a large degree of ambiguity: common words need to be distinguished from proper names
(geo/non-geo ambiguity), and the mapping between names and locations is ambiguous (London
can refer to the capital of the UK or to London, Ontario, Canada, or to about forty other
Londons on earth). In addition, names of places and the boundaries referred to change over
time, and databases are incomplete.
Objective. I investigate how referentially ambiguous spatial named entities can be grounded,
or resolved, with respect to an extensional coordinate model robustly on open-domain news
text.
I begin by comparing the few algorithms proposed in the literature, and, comparing semiformal,
reconstructed descriptions of them, I factor out a shared repertoire of linguistic heuristics
(e.g. rules, patterns) and extra-linguistic knowledge sources (e.g. population sizes). I then
investigate how to combine these sources of evidence to obtain a superior method. I also investigate
the noise effect introduced by the named entity tagging step that toponym resolution
relies on in a sequential system pipeline architecture.
Scope. In this thesis, I investigate a present-day snapshot of terrestrial geography as represented
in the gazetteer defined and, accordingly, a collection of present-day news text. I limit
the investigation to populated places; geo-coding of artifact names (e.g. airports or bridges),
compositional geographic descriptions (e.g. 40 miles SW of London, near Berlin), for instance,
is not attempted. Historic change is a major factor affecting gazetteer construction and ultimately
toponym resolution. However, this is beyond the scope of this thesis.
Method. While a small number of previous attempts have been made to solve the toponym
resolution problem, these were either not evaluated, or evaluation was done by manual inspection
of system output instead of curating a reusable reference corpus.
Since the relevant literature is scattered across several disciplines (GIS, digital libraries,
information retrieval, natural language processing) and descriptions of algorithms are mostly
given in informal prose, I attempt to systematically describe them and aim at a reconstruction
in a uniform, semi-formal pseudo-code notation for easier re-implementation. A systematic
comparison leads to an inventory of heuristics and other sources of evidence.
In order to carry out a comparative evaluation procedure, an evaluation resource is required.
Unfortunately, to date no gold standard has been curated in the research community. To this
end, a reference gazetteer and an associated novel reference corpus with human-labeled referent
annotation are created.
These are subsequently used to benchmark a selection of the reconstructed algorithms and
a novel re-combination of the heuristics catalogued in the inventory.
I then compare the performance of the same TR algorithms under three different conditions,
namely applying it to the (i) output of human named entity annotation, (ii) automatic annotation
using an existing Maximum Entropy sequence tagging model, and (iii) a na¨ıve toponym lookup
procedure in a gazetteer.
Evaluation. The algorithms implemented in this thesis are evaluated in an intrinsic or
component evaluation. To this end, we define a task-specific matching criterion to be used with
traditional Precision (P) and Recall (R) evaluation metrics. This matching criterion is lenient
with respect to numerical gazetteer imprecision in situations where one toponym instance is
marked up with different gazetteer entries in the gold standard and the test set, respectively, but
where these refer to the same candidate referent, caused by multiple near-duplicate entries in
the reference gazetteer.
Main Contributions. The major contributions of this thesis are as follows:
• A new reference corpus in which instances of location named entities have been manually
annotated with spatial grounding information for populated places, and an associated
reference gazetteer, from which the assigned candidate referents are chosen. This reference
gazetteer provides numerical latitude/longitude coordinates (such as 51320 North,
0 50 West) as well as hierarchical path descriptions (such as London > UK) with respect
to a world wide-coverage, geographic taxonomy constructed by combining several large,
but noisy gazetteers. This corpus contains news stories and comprises two sub-corpora,
a subset of the REUTERS RCV1 news corpus used for the CoNLL shared task (Tjong
Kim Sang and De Meulder (2003)), and a subset of the Fourth Message Understanding
Contest (MUC-4; Chinchor (1995)), both available pre-annotated with gold-standard.
This corpus will be made available as a reference evaluation resource;
• a new method and implemented system to resolve toponyms that is capable of robustly
processing unseen text (open-domain online newswire text) and grounding toponym instances
in an extensional model using longitude and latitude coordinates and hierarchical
path descriptions, using internal (textual) and external (gazetteer) evidence;
• an empirical analysis of the relative utility of various heuristic biases and other sources
of evidence with respect to the toponym resolution task when analysing free news genre
text;
• a comparison between a replicated method as described in the literature, which functions
as a baseline, and a novel algorithm based on minimality heuristics; and
• several exemplary prototypical applications to show how the resulting toponym resolution
methods can be used to create visual surrogates for news stories, a geographic exploration
tool for news browsing, geographically-aware document retrieval and to answer
spatial questions (How far...?) in an open-domain question answering system. These
applications only have demonstrative character, as a thorough quantitative, task-based
(extrinsic) evaluation of the utility of automatic toponym resolution is beyond the scope of this thesis and left for future work