1,202 research outputs found
Selective Sampling for Example-based Word Sense Disambiguation
This paper proposes an efficient example sampling method for example-based
word sense disambiguation systems. To construct a database of practical size, a
considerable overhead for manual sense disambiguation (overhead for
supervision) is required. In addition, the time complexity of searching a
large-sized database poses a considerable problem (overhead for search). To
counter these problems, our method selectively samples a smaller-sized
effective subset from a given example set for use in word sense disambiguation.
Our method is characterized by the reliance on the notion of training utility:
the degree to which each example is informative for future example sampling
when used for the training of the system. The system progressively collects
examples by selecting those with greatest utility. The paper reports the
effectiveness of our method through experiments on about one thousand
sentences. Compared to experiments with other example sampling methods, our
method reduced both the overhead for supervision and the overhead for search,
without the degeneration of the performance of the system.Comment: 25 pages, 14 Postscript figure
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
Homograph Disambiguation Through Selective Diacritic Restoration
Lexical ambiguity, a challenging phenomenon in all natural languages, is
particularly prevalent for languages with diacritics that tend to be omitted in
writing, such as Arabic. Omitting diacritics leads to an increase in the number
of homographs: different words with the same spelling. Diacritic restoration
could theoretically help disambiguate these words, but in practice, the
increase in overall sparsity leads to performance degradation in NLP
applications. In this paper, we propose approaches for automatically marking a
subset of words for diacritic restoration, which leads to selective homograph
disambiguation. Compared to full or no diacritic restoration, these approaches
yield selectively-diacritized datasets that balance sparsity and lexical
disambiguation. We evaluate the various selection strategies extrinsically on
several downstream applications: neural machine translation, part-of-speech
tagging, and semantic textual similarity. Our experiments on Arabic show
promising results, where our devised strategies on selective diacritization
lead to a more balanced and consistent performance in downstream applications.Comment: accepted in WANLP 201
A literature survey of active machine learning in the context of natural language processing
Active learning is a supervised machine learning technique in which the learner is in control of the data used for learning. That control is utilized by the learner to ask an oracle, typically a human with extensive knowledge of the domain at hand, about the classes of the instances for which the model learned so far makes unreliable predictions. The active learning process takes as input a set of labeled examples, as well as a larger set of unlabeled examples, and produces a classifier and a relatively small set of newly labeled data. The overall goal is to create as good a classifier as possible, without having to mark-up and supply the learner with more data than necessary. The learning process aims at keeping the human annotation effort to a minimum, only asking for advice where the training utility of the result of such a query is high. Active learning has been successfully applied to a number of natural language processing tasks, such as, information extraction, named entity recognition, text categorization, part-of-speech tagging, parsing, and word sense disambiguation. This report is a literature survey of active learning from the perspective of natural language processing
Automatic summarising: factors and directions
This position paper suggests that progress with automatic summarising demands
a better research methodology and a carefully focussed research strategy. In
order to develop effective procedures it is necessary to identify and respond
to the context factors, i.e. input, purpose, and output factors, that bear on
summarising and its evaluation. The paper analyses and illustrates these
factors and their implications for evaluation. It then argues that this
analysis, together with the state of the art and the intrinsic difficulty of
summarising, imply a nearer-term strategy concentrating on shallow, but not
surface, text analysis and on indicative summarising. This is illustrated with
current work, from which a potentially productive research programme can be
developed
Web knowledge bases
Knowledge is key to natural language understanding. References to specific people, places and things in text are crucial to resolving ambiguity and extracting meaning. Knowledge Bases (KBs) codify this information for automated systems β enabling applications such as entity-based search and question answering. This thesis explores the idea that sites on the web may act as a KB, even if that is not their primary intent. Dedicated kbs like Wikipedia are a rich source of entity information, but are built and maintained at an ongoing cost in human effort. As a result, they are generally limited in terms of the breadth and depth of knowledge they index about entities. Web knowledge bases offer a distributed solution to the problem of aggregating entity knowledge. Social networks aggregate content about people, news sites describe events with tags for organizations and locations, and a diverse assortment of web directories aggregate statistics and summaries for long-tail entities notable within niche movie, musical and sporting domains. We aim to develop the potential of these resources for both web-centric entity Information Extraction (IE) and structured KB population. We first investigate the problem of Named Entity Linking (NEL), where systems must resolve ambiguous mentions of entities in text to their corresponding node in a structured KB. We demonstrate that entity disambiguation models derived from inbound web links to Wikipedia are able to complement and in some cases completely replace the role of resources typically derived from the KB. Building on this work, we observe that any page on the web which reliably disambiguates inbound web links may act as an aggregation point for entity knowledge. To uncover these resources, we formalize the task of Web Knowledge Base Discovery (KBD) and develop a system to automatically infer the existence of KB-like endpoints on the web. While extending our framework to multiple KBs increases the breadth of available entity knowledge, we must still consolidate references to the same entity across different web KBs. We investigate this task of Cross-KB Coreference Resolution (KB-Coref) and develop models for efficiently clustering coreferent endpoints across web-scale document collections. Finally, assessing the gap between unstructured web knowledge resources and those of a typical KB, we develop a neural machine translation approach which transforms entity knowledge between unstructured textual mentions and traditional KB structures. The web has great potential as a source of entity knowledge. In this thesis we aim to first discover, distill and finally transform this knowledge into forms which will ultimately be useful in downstream language understanding tasks
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