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We present a simple method to find topics in user reviews that accompany ratings for products or services. Standard topic analysis will perform sub-optimal on such data since the word distributions in the documents are not only determined by the topics but by the sentiment as well. We reduce the influence of the sentiment on the topic selection by adding two explicit topics, representing positive and negative sentiment. We evaluate the proposed method on a set of over 15,000 hospital reviews. We show that the proposed method, Latent Semantic Analysis with explicit word features, finds topics with a much smaller bias for sentiments than other similar methods.
Concreteness of words has been measured and used in psycholinguistics already for decades. Recently, it is also used in retrieval and NLP tasks. For English a number of well known datasets has been established with average values for perceived concreteness.
We give an overview of available datasets for German, their correlation and evaluate prediction algorithms for concreteness of German words. We show that these algorithms achieve similar results as for English datasets. Moreover, we show for all datasets there are no significant differences between a prediction model based on a regression model using word embeddings as features and a prediction algorithm based on word similarity according to the same embeddings.
In order to ensure validity in legal texts like contracts and case law, lawyers rely on standardised formulations that are written carefully but also represent a kind of code with a meaning and function known to all legal experts. Using directed (acyclic) graphs to represent standardized text fragments, we are able to capture variations concerning time specifications, slight rephrasings, names, places and also OCR errors. We show how we can find such text fragments by sentence clustering, pattern detection and clustering patterns. To test the proposed methods, we use two corpora of German contracts and court decisions, specially compiled for this purpose. However, the entire process for representing standardised text fragments is language-agnostic. We analyze and compare both corpora and give an quantitative and qualitative analysis of the text fragments found and present a number of examples from both corpora.
Library of Congress Subject Headings (LCSH) are popular for indexing library records. We studied the possibility of assigning LCSH automatically by training classifiers for terms used frequently in a large collection of abstracts of the literature on hand and by extracting headings from those abstracts. The resulting classifiers reach an acceptable level of precision, but fail in terms of recall partly because we could only train classifiers for a small number of LCSH. Extraction, i.e., the matching of headings in the text, produces better recall but extremely low precision. We found that combining both methods leads to a significant improvement of recall and a slight improvement of F1 score with only a small decrease in precision.
Lemmatization is a central task in many NLP applications. Despite this importance, the number of (freely) available and easy to use tools for German is very limited. To fill this gap, we developed a simple lemmatizer that can be trained on any lemmatized corpus. For a full form word the tagger tries to find the sequence of morphemes that is most likely to generate that word. From this sequence of tags we can easily derive the stem, the lemma and the part of speech (PoS) of the word. We show (i) that the quality of this approach is comparable to state of the art methods and (ii) that we can improve the results of Part-of-Speech (PoS) tagging when we include the morphological analysis of each word.
In the present paper we sketch an automated procedure to compare different versions of a contract. The contract texts used for this purpose are structurally differently composed PDF files that are converted into structured XML files by identifying and classifying text boxes. A classifier trained on manually annotated contracts achieves an accuracy of 87% on this task. We align contract versions and classify aligned text fragments into different similarity classes that enhance the manual comparison of changes in document versions. The main challenges are to deal with OCR errors and different layout of identical or similar texts. We demonstrate the procedure using some freely available contracts from the City of Hamburg written in German. The methods, however, are language agnostic and can be applied to other contracts as well.
Concreteness of words has been studied extensively in psycholinguistic literature. A number of datasets have been created with average values for perceived concreteness of words. We show that we can train a regression model on these data, using word embeddings and morphological features, that can predict these concreteness values with high accuracy. We evaluate the model on 7 publicly available datasets. Only for a few small subsets of these datasets prediction of concreteness values are found in the literature. Our results clearly outperform the reported results for these datasets.
For the analysis of contract texts, validated model texts, such as model clauses, can be used to identify used contract clauses. This paper investigates how the similarity between titles of model clauses and headings extracted from contracts can be computed, and which similarity measure is most suitable for this. For the calculation of the similarities between title pairs we tested various variants of string similarity and token based similarity. We also compare two additional semantic similarity measures based on word embeddings using pre-trained embeddings and word embeddings trained on contract texts. The identification of the model clause title can be used as a starting point for the mapping of clauses found in contracts to verified clauses.
Scientific papers from all disciplines contain many abbreviations and acronyms. In many cases these acronyms are ambiguous. We present a method to choose the contextual correct definition of an acronym that does not require training for each acronym and thus can be applied to a large number of different acronyms with only few instances. We constructed a set of 19,954 examples of 4,365 ambiguous acronyms from image captions in scientific papers along with their contextually correct definition from different domains. We learn word embeddings for all words in the corpus and compare the averaged context vector of the words in the expansion of an acronym with the weighted average vector of the words in the context of the acronym. We show that this method clearly outperforms (classical) cosine similarity. Furthermore, we show that word embeddings learned from a 1 billion word corpus of scientific exts outperform word embeddings learned from much larger general corpora.
This paper summarizes the results of a comprehensive statistical analysis on a corpus of open access articles and contained figures. It gives an insight into quantitative relationships between illustrations or types of illustrations, caption lengths, subjects, publishers, author affiliations, article citations and others.