1,497 research outputs found
Miracleâs 2005 Approach to Monolingual Information Retrieval
This paper presents the 2005 Miracleâs team approach to Monolingual Information Retrieval. The goal for the experiments in this year was twofold: continue testing the effect of combination approaches on information retrieval tasks, and improving our basic processing and indexing tools, adapting them to new languages with strange encoding schemes. The starting point was a set of basic components: stemming, transforming, filtering, proper nouns extracting, paragraph extracting, and pseudo-relevance feedback. Some of these basic components were used in different combinations and order of application for document indexing and for query processing. Second order combinations were also tested, by averaging or selective combination of the documents retrieved by different approaches for a particular query
Thematic Annotation: extracting concepts out of documents
Contrarily to standard approaches to topic annotation, the technique used in
this work does not centrally rely on some sort of -- possibly statistical --
keyword extraction. In fact, the proposed annotation algorithm uses a large
scale semantic database -- the EDR Electronic Dictionary -- that provides a
concept hierarchy based on hyponym and hypernym relations. This concept
hierarchy is used to generate a synthetic representation of the document by
aggregating the words present in topically homogeneous document segments into a
set of concepts best preserving the document's content.
This new extraction technique uses an unexplored approach to topic selection.
Instead of using semantic similarity measures based on a semantic resource, the
later is processed to extract the part of the conceptual hierarchy relevant to
the document content. Then this conceptual hierarchy is searched to extract the
most relevant set of concepts to represent the topics discussed in the
document. Notice that this algorithm is able to extract generic concepts that
are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
LORE: a model for the detection of fine-grained locative references in tweets
[EN] Extracting geospatially rich knowledge from tweets is of utmost importance for location-based systems in emergency services to raise situational awareness about a given crisis-related incident, such as earthquakes, floods, car accidents, terrorist attacks, shooting attacks, etc. The problem is that the majority of tweets are not geotagged, so we need to resort to the messages in the search of geospatial evidence. In this context, we present LORE, a location-detection system for tweets that leverages the geographic database GeoNames together with linguistic knowledge through NLP techniques. One of the main contributions of this model is to capture fine-grained complex locative references, ranging from geopolitical entities and natural geographic references to points of interest and traffic ways. LORE outperforms state-of-the-art open-source location-extraction systems (i.e. Stanford NER, spaCy, NLTK and OpenNLP), achieving an unprecedented trade-off between precision and recall. Therefore, our model provides not only a quantitative advantage over other well-known systems in terms of performance but also a qualitative advantage in terms of the diversity and semantic granularity of the locative references extracted from the tweets.Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], and the European Union's Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. We also thank Universidad de Granada for their financial support to the first author through the Becas de Iniciacion para estudiantes de Master 2018 del Plan Propio de la UGR.FernĂĄndez-MartĂnez, NJ.; Periñån-Pascual, C. (2021). LORE: a model for the detection of fine-grained locative references in tweets. OnomĂĄzein. (52):195-225. https://doi.org/10.7764/onomazein.52.111952255
Dublin City University at QA@CLEF 2008
We describe our participation in Multilingual Question Answering at CLEF 2008 using German and English as our source and target languages respectively. The system was built using UIMA (Unstructured Information Management Architecture) as underlying framework
MIRACLE at Ad-Hoc CLEF 2005: Merging and Combining Without Using a Single Approach
This paper presents the 2005 Miracleâs team approach to the Ad-Hoc Information Retrieval tasks. The goal for the experiments this year was twofold: to continue testing the effect of combination approaches on information retrieval tasks, and improving our basic processing and indexing tools, adapting them to new languages with strange encoding schemes. The starting point was a set of basic components: stemming, transforming, filtering, proper nouns extraction, paragraph extraction, and pseudo-relevance feedback. Some of these basic components were used in different combinations and order of application for document indexing and for query processing. Second-order combinations were also tested, by averaging or selective combination of the documents retrieved by different approaches for a particular query. In the multilingual track, we concentrated our work on the merging process of the results of monolingual runs to get the overall multilingual result, relying on available translations. In both cross-lingual tracks, we have used available translation resources, and in some cases we have used a combination approach
Rapport : a fact-based question answering system for portuguese
Question answering is one of the longest-standing problems in natural language processing. Although natural language interfaces for computer systems can be considered
more common these days, the same still does not happen regarding access to specific
textual information. Any full text search engine can easily retrieve documents containing user specified or closely related terms, however it is typically unable to answer user
questions with small passages or short answers.
The problem with question answering is that text is hard to process, due to its syntactic structure and, to a higher degree, to its semantic contents. At the sentence level,
although the syntactic aspects of natural language have well known rules, the size and
complexity of a sentence may make it difficult to analyze its structure. Furthermore, semantic aspects are still arduous to address, with text ambiguity being one of the hardest
tasks to handle. There is also the need to correctly process the question in order to define its target, and then select and process the answers found in a text. Additionally, the
selected text that may yield the answer to a given question must be further processed
in order to present just a passage instead of the full text. These issues take also longer
to address in languages other than English, as is the case of Portuguese, that have a lot
less people working on them.
This work focuses on question answering for Portuguese. In other words, our field
of interest is in the presentation of short answers, passages, and possibly full sentences,
but not whole documents, to questions formulated using natural language. For that purpose, we have developed a system, RAPPORT, built upon the use of open information
extraction techniques for extracting triples, so called facts, characterizing information
on text files, and then storing and using them for answering user queries done in natural language. These facts, in the form of subject, predicate and object, alongside other
metadata, constitute the basis of the answers presented by the system. Facts work both
by storing short and direct information found in a text, typically entity related information, and by containing in themselves the answers to the questions already in the
form of small passages. As for the results, although there is margin for improvement,
they are a tangible proof of the adequacy of our approach and its different modules for
storing information and retrieving answers in question answering systems.
In the process, in addition to contributing with a new approach to question answering for Portuguese, and validating the application of open information extraction to
question answering, we have developed a set of tools that has been used in other natural language processing related works, such as is the case of a lemmatizer, LEMPORT,
which was built from scratch, and has a high accuracy. Many of these tools result from
the improvement of those found in the Apache OpenNLP toolkit, by pre-processing their
input, post-processing their output, or both, and by training models for use in those
tools or other, such as MaltParser. Other tools include the creation of interfaces for
other resources containing, for example, synonyms, hypernyms, hyponyms, or the creation of lists of, for instance, relations between verbs and agents, using rules
Semantic document indexing in ontology-driven organizational memories
Effective document retrieval using domain knowledge and semantics is one of the major challenges in Information Retrieval. Over the last years, there has been a growing interest in ontologies as an artifact for human knowledge representation and a critical component in Knowledge Management, Semantic Web, and Business-to-Business applications. We have found that it is not easy to represent certain types of knowledge (skills or procedures) or to transform certain types of knowledge representation (knowledge contained in diagrams) into an appropriate ontological format. To overcome this problem, our proposal is to connect knowledge sources to the domain ontology associated with an Organizational Memory without forcing any transformation in the structure of the source itself. This connection will allow the semantic classification of knowledge sources so that when a user performs a query it is possible to recover the documents that have a higher probability of containing the answer.II Workshop de IngenierĂa de Software y Bases de Datos (WISBD)Red de Universidades con Carreras en InformĂĄtica (RedUNCI
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