We review recent evidence indicating that researchers in experimental psychology may have used suboptimal estimates of word frequency. Word frequency measures should be based on a corpus of at least 20 million words that contains language participants in psychology experiments are likely to have been exposed to. In addition, the quality of word frequency measures should be ascertained by correlating them with behavioral word processing data. When we apply these criteria to the word frequency measures available for the German language, we find that the commonly used Celex frequencies are the least powerful to predict lexical decision times. Better results are obtained with the Leipzig frequencies, the dlexDB frequencies, and the Google Books 2000-2009 frequencies. However, as in other languages the best performance is observed with subtitle-based word frequencies. The SUBTLEX-DE word frequencies collected for the present ms are made available in easy-to-use files and are free for educational purposes.

The Word Frequency Effect A Review of Recent Developments and Implications for the Choice of Frequency Estimates in German

https://leseprobe.buch.de/images-adb/a8/ce/a8ce578b-41ae-4377-b869-b8993abcf1f3.pdf

Lexikon der Sprachwissenschaft

Zusammenfassung. Mit der lexikalischen Datenbank dlexDB stellen wir der psychologischen und linguistischen Forschung im World Wide Web online statistische Kennwerte fur eine Vielzahl von verarbeitungsrelevanten Merkmalen von Wortern zur Verfugung. Diese Kennwerte umfassen die durch CELEX (Baayen, Piepenbrock und Gulikers, 1995) bekannten Variablen der Haufigkeiten von Wortformen und Lemmata in Texten geschriebener Sprache. Daruber hinaus berechnen wir eine Reihe neuer Kennwerte wie die Haufigkeiten von Silben, Morphemen, Zeichenfolgen und Mehrwortverbindungen sowie Wortahnlichkeitsmase. Die Datengrundlage bildet das Kernkorpus des Digitalen Worterbuchs der deutschen Sprache (DWDS) mit uber 100 Millionen laufenden Wortern. Wir illustrieren die Validitat dieser Kennwerte mit neuen Ergebnissen zu ihrem Einfluss auf Fixationsdauern beim Lesen von Satzen.

dlexDB : eine lexikalische Datenbank für die psychologische und linguistische Forschung

Receiving social feedback such as praise or blame for one's character traits is a key component of everyday human interactions. It has been proposed that humans are positively biased when integrating social feedback into their self-concept. However, a mechanistic description of how humans process self-relevant feedback is lacking. Here, participants received feedback from peers after a real-life interaction. Participants processed feedback in a positively biased way, i.e., they changed their self-evaluations more toward desirable than toward undesirable feedback. Using functional magnetic resonance imaging we investigated two feedback components. First, the reward-related component correlated with activity in ventral striatum and in anterior cingulate cortex/medial prefrontal cortex (ACC/MPFC). Second, the comparison-related component correlated with activity in the mentalizing network, including the MPFC, the temporoparietal junction, the superior temporal sulcus, the temporal pole, and the inferior frontal gyrus. This comparison-related activity within the mentalizing system has a parsimonious interpretation, i.e., activity correlated with the differences between participants' own evaluation and feedback. Importantly, activity within the MPFC that integrated reward-related and comparison-related components predicted the self-related positive updating bias across participants offering a mechanistic account of positively biased feedback processing. Thus, theories on both reward and mentalizing are important for a better understanding of how social information is integrated into the human self-concept.

https://www.jneurosci.org/content/jneuro/32/47/16832.full.pdf

Positively Biased Processing of Self-Relevant Social Feedback

This article introduces childLex, an online database of German read by children. childLex is based on a corpus of children's books and comprises 10 million words that were syntactically annotated and lemmatized. childLex reports linguistic norms for lexical, superlexical, and sublexical variables in three different age groups: 6-8 (grades 1-2), 9-10 (grades 3-4), and 11-12 years (grades 5-6). Here, we describe how childLex was collected and analyzed. In addition, we provide information about the distributions of word frequency, word length, and orthographic neighborhood size, as well as their intercorrelations. Finally, we explain how childLex can be accessed using a Web interface.

childLex: A lexical database of German read by children

TAGH is a system for automatic recognition of German word forms. It is based on a stem lexicon with allomorphs and a concatenative mechanism for inflection and word formation. Weighted FSA and a cost function are used in order to determine the correct segmentation of complex forms: the correct segmentation for a given compound is supposed to be the one with the least cost. TAGH is based on a large stem lexicon of almost 80.000 stems that was compiled within 5 years on the basis of large newspaper corpora and literary texts. The number of analyzable word forms is increased considerably by more than 1000 different rules for derivational and compositional word formation. The recognition rate of TAGH is more than 99% for modern newspaper text and approximately 98.5% for literary texts.

TAGH : A complete morphology for german based on weighted finite state automata

The lexical database dlexDB supplies in form of an online database frequency-based norms of numerous process-related word properties for psychological and linguistic research. These values include well known variables such as printed frequency of word form and lemma as documented also in CELEX (Baayen, Piepenbrock und Gulikers, 1995). In addition, we compute new values like frequencies based on syllables, and morphemes as well as frequencies of character chains, and multiple word combinations. The statistics are based on the Kernkorpus des Digitalen Wrterbuchs der deutschen Sprache (DWDS) with over 100 million running words. We illustrate the validity of these norms with new results about fixation durations in sentence reading.

dlexDB - A lexical database for the psychological and linguistic research

We combine a surface based approach to discourse parsing with an explicit rhetorical grammar in order to efficiently construct an underspecified representation of possible discourse structures.

/pdf/rhetorical-parsing-with-underspecification-and-forests-vnthhs4stn.pdf

Rhetorical parsing with underspecification and forests

Previously, there has been a lack for an automatic analyser and generator for the word forms of Runyakitara. In this paper, we present a computational model for grammatical Runyakitara verbs. This model, RUNYAGRAM, is based on freely-available open-sourced finite-state methods and, in particular, the fsm2 interpreter. It captures the morphotactic structures with non-recursive context -free grammars supported by fsm2 and morpho-phonological alternations with a finite composition of commonly used context-dependent string rewriting rules. Their combination results into a finite state transducer that can be exported and used in numberless software-developing platforms. The obtained transducer is an important building-block that can be employed in comprehensive morphological analysers, syntactic parsers, spell-checkers, text-to-speech synthesizers, and machine translation systems. Currently, 86% of the verb forms are recognized. It is possible to increase the coverage, or alternatively, to adapt the approach of the RUNYAGRAM system to related languages.

Finite State Methods in Morphological Analysis of Runyakitara Verbs

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