[Resident Evil 4 Ultimate Item Modifier V1 1
Tilo Chopin
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residentevilmodding.boards.net Ultimate Item Modifier v1.3 Resident Evil 4 Ultimate Item Modifier v1.3 by wilsonso This is a new version that saves the relative pointer OFFSET instead of the absolute pointer ADDRESS. What's more, it can save your settings!
This paper explores verb-attached PPs as adjuncts or parts of verbal frames with the help of a large-scale valency-database generated from the output of a dependency parser. Our investigation is based on more than 240 million words of American and British English. We combine measures of surprise with measures of lexical diversity in order to study fixedness of various types. We also use these measures to explore the cline from verbal complement to adjunct and test statistical measures of surprise as a means of distinguishing complements from adjuncts. We calculate measures of surprise and variability for verb-preposition, verb-object-PP and other combinations in order to identify and locate verbal idioms.
In this paper we present a parser based analysis of verb-attached prepositional phrases. We investigate fixedness and idiomaticity of verb-attached PP structures and explore the cline from verbal idioms to verbal frames to adverbials based on measures of association and measures of variation. Specifically, we do this by observing the combinations of lexical items found in the verb-attached prepositional phrase structures located in the syntactic analysis produced by the parser.
Syntactically annotated data offers the unique possibility of selecting syntactic structures in abstraction from lexis. The use of large-scale automatic annotation with syntactic information has only recently become possible. Van Noord and Bouma (2009) find that:
Knowledge-based parsers are now accurate, fast and robust enough to be used to obtain syntactic annotations for very large corpora fully automatically. We argued that such parsed corpora are an interesting new resource for linguists. (van Noord & Bouma 2009: 37)
In Lehmann and Schneider (2009), we have investigated subject-verb-object structures. However, prepositional phrase attachment is a more problematic area in terms of parser performance. In addition to presenting new data on verb-attached prepositional phrases, we also test fully automatically parsed data for its potential in an area where parser performance must be regarded as problematic.
In section 2 we discuss the measures of surprise and variability used for ranking lexical types. In section 3 we introduce the data analysed and discuss the process of annotation. Section 4 documents the extraction of verb-attached PPs from the annotated material. Section 5 presents the results achieved by the application of the measures of surprise and variability introduced in section 2 to the structures extracted in section 4. In section 6 we discuss our results and our methodological approach.
Collocations and idiomaticity are prime examples of areas of gradience and of grammar and lexis in co-operation. The concepts of collocation, fixedness, idiomaticity and non-compositionality are often described as closely related. For example, Fernando (1996) states:
[W]hile habitual co-occurrence produces idiomatic expressions, both canonical and non-canonical, only those expressions which become conventionally fixed in a specific order and lexical form, or have a restricted set of variants, acquire the status of idioms. (Fernando 1996: 31)
In this view, idioms are a subset of collocation and the touchstone for idiomaticity is typically non-compositionality (see e.g. Fernando & Flavell 1981: 17). Non-compositionality is difficult to measure in an automatic, corpus-based approach, but non-compositionality has a strong impact on fixedness in the following two ways: First, on the paradigmatic axis, non-compositional expressions have very limited lexical choice: the expression kick the bucket cannot be rephrased using synonyms, e.g. kick the bin or hit the bucket. Limited lexical choice often leads to a strong association between the elements forming an idiom, which can be easily detected by collocation measures (which we treat in section 2.1). Second, on the syntagmatic axis, syntactic variability is restricted. Idiomatic expressions have high fixedness (which we treat in section 2.2). The phrase kick a bucket is as unusual as kick the large bucket, only very few modifications such as kick the proverbial bucket are usually found in large text collections. The close connection between compositionality and fixedness has e.g. been demonstrated by Gibbs and Nayak (1989) who have shown that native speakers judge non-compositional idioms as less syntactically flexible than partly compositional idioms. We therefore use fixedness and co-occurrence measures as proxies to non-compositionality.
Modifiability expresses how far the idiom components can be modified by adjectives, determiners etc. Completely fixed collocations, in which no participant is ever modified, or always modified by the same word, are extremely rare (see e.g. Barlow 2000). We thus need a measure that expresses the lexical variation among the modifiers, including null-modifiers as tokens of absent modification. We have split modification into nominal modification (adjectives, non-head nouns) and determiner-modification (articles).
Looking at triples like take into consideration, we can take stock of all the determiners and modifiers that accompany the description noun consideration. A frequently considered possibility for measuring the variability of such slots is the ratio of types per tokens. Type-token ratio (TTR) is a popular measure for expressing lexical variation. Low and high TTRs imply a low respectively high level of lexical diversity.
Although TTR is a useful measure for variability on texts of equal length, it has a major disadvantage: it is dependent on the number of tokens observed: any text of 5 words will almost certainly have TTR = 1, any text of 1000 words almost certainly a TTR that is considerably lower. We expect a curve that, in relation to text length, increasingly flattens out but never stops falling slightly, even for extremely large texts. Figure 1 shows TTRs in The [London] Times Corpus for the whole paper and its sub-sections plotted from word 1 to word 250,000. We can observe a steep fall from the first tokens observed. It is thus obvious that comparing TTRs at different token counts is not possible. Such a comparison, however, is certainly desirable, given the difference in lexical diversity observable between the different sections.
It would thus be necessary to compare texts of equal length or lists containing the same number of tokens. This is not always possible, and if one does so, one discards much information and thus increases data sparseness to the level of the common denominator. When comparing the TTR of different texts, this is the length of the shortest text in the comparison. There are many applications in which the effect is even more serious. Let us look at the example of an application investigating the contexts of word-word combinations, where the TTR of the context words that are found can serve as a variability measure (we will in fact do so in section 5). The rarest word-word combinations in such a comparison are hapax legomena, i.e. word-word combinations that occur only once. Discarding all but one occurrence for each combination would be the common denominator, but at the same time this would entail giving up measuring frequencies of occurrence and variation, which would precisely be the purpose of the exercise. Using a more pragmatic approach, one could set a threshold and use this as denominator (e.g. only keep 10 occurrences, and only of word-word combinations that occur at least 10 times), but if one sets a high threshold all relatively rare combinations are completely discarded, and if one sets a low threshold only a small random sample of the frequently occurring combinations can be considered.
We use O/E as a collocation measure. O/E expresses the frequency of an observed event O divided by its expected frequency E. E calculates a homogenous distribution of words, for example in our investigation of VPN triplets, independent probabilities of drawing V from an urn containing all the verbs, P from an urn containing all the prepositions, and N from an urn containing all the nouns that we have observed in VPN structures. In other words, E simulates a situation of completely random lexical combinations in the observed verb- preposition-description noun syntagma. O/E expresses how many times more frequent a VPN triplet occurs than in random lexical combination, and thus serves as an association measure. Associations may of course both be semantic (selectional restrictions) or idiomatic in nature.
Our choice of O/E is based on the following reasons. First, it has a clear probabilistic definition and is directly related to information-theoretic measures of surprise such as mutual information. Second, it is a measure of surprise, which means that also rare, very sparse collocations can be detected. In window-based approaches (see section 4), the vast majority of the top-ranked O/E entries are garbage. Approaches based on parsed corpora provide considerably cleaner data (see e.g. Seretan & Wehrli 2006), as we discuss in section 2.4. Third, it is a simple and straightforward probabilistic measure that is easy to interpret.
As a probabilistic measure of surprise, O/E does not express statistical significance. As such it has no bias towards frequent items. In contrast, it will inevitably result in high values for rare combinations of rare items. In order to eliminate random noise introduced by tagging, chunking and parsing errors, we employ a significance test. We measure the statistical significance of observations with the t-score test. Of course, statistical significance can be tested with a large range of significance tests: Berry-Rogghe (1974) used the z-score, Wulff (2008) uses the Fisher Yates exact test. We use the t-score. The t-score test is frequently used. Evert (2009) describes it as a version of the z-score that heuristically corrects its disadvantage.
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