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We present an approach for automatic detection and correction of OCR-induced misspellings in historical texts. The main objective is the post-correction of the digitized Royal Society Corpus, a set of historical documents from 1665 to 1869. Due to the aged material the OCR procedure has made mistakes, thus leading to files corrupted by thousands of misspellings. This motivates a post processing step. The current correction technique is a pattern-based approach which due to its lack of generalization suffers from bad recall.
To generalize from the patterns we propose to use the noisy channel model. From the pattern based substitutions we train a corpus specific error model complemented with a language model. With an F1-Score of 0.61 the presented technique significantly outperforms the pattern based approach which has an F1-score of 0.28. Due to its more accurate error model it also outperforms other implementations of the noisy channel model.
We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-supervised graph clustering algorithm, we also examine how distributional information is best added to that classifier. Our experiments are carried out on 5 different food categorization tasks.
We examine the task of relation extraction in the food domain by employing distant supervision. We focus on the extraction of two relations that are not only relevant to product recommendation in the food domain, but that also have significance in other domains, such as the fashion or electronics domain. In order to select suitable training data, we investigate various degrees of freedom. We consider three processing levels being argument level, sentence level and feature level. As external resources, we employ manually created surface patterns and semantic types on all these levels. We also explore in how far rule-based methods employing the same information are competitive.
In this paper, we examine methods to extract different domain-specific relations from the food domain. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on different parts of a document. We show that the effectiveness of a particular method depends very much on the relation type considered and that there is no single method that works equally well for every relation type. As we need to process a large amount of unlabeled data our methods only require a low level of linguistic processing. This has also the advantage that these methods can provide responses in real time.
Graphenbasierte Ansätze spielen in der digitalen Lexikografie eine immer stärkere Rolle. Essentiell für die Erstellung, Verwaltung und Nutzung graphenbasierter lexikografischer Ressourcen ist jedoch eine leistungsfähige und zugleich einfach zu handhabende Zugriffsstruktur, die Suchen nach komplexen Konstellationen in solchen Graphen ermöglicht. Für heutige Graphendatenbanken stehen zahlreiche Abfragesprachen zur Verfügung, deren Verwendung jedoch verhältnismäßig voraussetzungsreich ist.
Das Poster stellt einen webbasierten, frei konfigurierbaren Query Builder vor, der die Formulierung semantisch sehr komplexer Suchabfragen an eine (mit dem Tinkerpop-Standard kompatible) Property-Graphendatenbank ermöglicht. Die Abfrage erfolgt durch einfaches visuell-interaktives Zusammenstellen hierarchisch angeordneter Abfrageelemente und liefert Antworten in Echtzeit. Dabei wird von den Komplexitäten der verwendeten Low-level-Abfragesprache Gremlin abstrahiert. Der Query Builder ist ein zentrales Modul eines derzeit entwickelten Open-Source-Softwaresystems zur Verwaltung und Online-Publikation graph-erweiterter lexikografischer Ressourcen.
The Stuttgart-Tübingen Tagset (STTS) is a widely used POS annotation scheme for German which provides 54 different tags for the analysis on the part of speech level. The tagset, however, does not distinguish between adverbs and different types of particles used for expressing modality, intensity, graduation, or to mark the focus of the sentence. In the paper, we present an extension to the STTS which provides tags for a more fine-grained analysis of modification, based on a syntactic perspective on parts of speech. We argue that the new classification not only enables us to do corpus-based linguistic studies on modification, but also improves statistical parsing. We give proof of concept by training a data-driven dependency parser on data from the TiGer treebank, providing the parser a) with the original STTS tags and b) with the new tags. Results show an improved labelled accuracy for the new, syntactically motivated classification.
Reframing FrameNet Data
(2004)
The Berkeley FrameNet Project (http://www.icsi.berkeley.edu/~framenet) is building an on-line lexical resource for contemporary English. The database provides information about the semantic and syntactic combinatorial possibilities (valences) of each item analyzed. This paper describes the conceptual basis for what has been called reframing of data in the FrameNet database and exemplifies two new frame-to-frame relations, Causative_of and Inchoative_of, the implementation of which came about as a result of reanalysis of certain frames and lexical units. The new relations are characterized with respect to a triple of frames involving the notion of attaching, and entering them into the database is demonstrated using the Frame Relations Editor. The two relations allow FrameNet to make frame-wise distinctions that capture fairly systematic semantic relationships across sets of lexical units. While the Inheritance and Subframe relations are of particular interest to the NLP research community, Causative_of and Inchoative_of may be more relevant to lexicography.
In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without deriving explicit features. The feature design for the detection of subjective expressions is fairly difficult and there currently exists no commonly accepted feature set. We consider various structures, such as constituency parse structures, dependency parse structures, and predicate-argument structures. In order to generalize from lexical information, we additionally augment these structures with clustering information and the task-specific knowledge of subjective words. The convolution kernels will be compared with a standard vector kernel.
We explore the feasibility of contextual healthiness classification of food items. We present a detailed analysis of the linguistic phenomena that need to be taken into consideration for this task based on a specially annotated corpus extracted from web forum entries. For automatic classification, we compare a supervised classifier and rule-based classification. Beyond linguistically motivated features that include sentiment information we also consider the prior healthiness of food items.