The growing availability of on-line textual sources and the potential number of applications of knowledge acquisition from textual data has lead to an increase in Information Extraction (IE) research. Some examples of these applications are the generation of data bases from documents, as well as the acquisition of knowledge useful for emerging technologies like question answering, information integration, and others related to text mining. However, one of the main drawbacks of the application of IE refers to its intrinsic domain dependence. For the sake of reducing the high cost of manually adapting IE applications to new domains, experiments with different Machine Learning (ML) techniques have been carried out by the research community. This survey describes and compares the main approaches to IE and the different ML techniques used to achieve Adaptive IE technology.

In this paper we present a semantic role labeling system submitted to the CoNLL-2005 shared task. The system makes use of partial and full syntactic information and converts the task into a sequential BIO-tagging. As a result, the labeling architecture is very simple . Building on a state-of-the-art set of features, a binary classifier for each label is trained using AdaBoost with fixed depth decision trees. The final system, which combines the outputs of two base systems performed F 1 =76.59 on the official test set. Additionally, we provide results comparing the system when using partial vs. full parsing input information. Goals and System ArchitectureThe goal of our work is twofold. On the one hand, we want to test whether it is possible to implement a competitive SRL system by reducing the task to a sequential tagging. On the other hand, we want to investigate the effect of replacing partial parsing information by full parsing. For that, we built two different individual systems with a shared sequential strategy but using UPC chunks-clauses, and Charniak's parses, respectively. We will refer to those systems as PP UPC and FP CHA , hereinafter.Both partial and full parsing annotations provided as input information are of hierarchical nature. Our system navigates through these syntactic structures in order to select a subset of constituents organized sequentially (i.e., non embedding). Propositions are treated independently, that is, each target verb generates a sequence of tokens to be annotated. We call this pre-processing step sequentialization.The sequential tokens are selected by exploring the sentence spans or regions defined by the clause boundaries 1 . The top-most syntactic constituents falling inside these regions are selected as tokens. Note that this strategy is independent of the input syntactic annotation explored, provided it contains clause boundaries. It happens that, in the case of full parses, this node selection strategy is equivalent to the pruning process defined by Xue and Palmer (2004), which selects sibling nodes along the path of ancestors from the verb predicate to the root of the tree 2 . Due to this pruning stage, the upper-bound recall figures are 95.67% for PP UPC and 90.32% for FP CHA . These values give F 1 performance upper bounds of 97.79 and 94.91, respectively, assuming perfect predictors (100% precision).The nodes selected are labeled with B-I-O tags depending if they are at the beginning, inside, or outside of a verb argument. There is a total of 37 argument types, which amount to 37*2+1=75 labels.Regarding the learning algorithm, we used generalized AdaBoost with real-valued weak classifiers, which constructs an ensemble of decision trees of fixed depth (Schapire and Singer, 1999). We considered a one-vs-all decomposition into binary prob-1 Regions to the right of the target verb corresponding to ancestor clauses are omitted in the case of partial parsing.2 With the unique exception of the exploration inside sibling PP constituents proposed by (Xue and Palmer, 2004). 193

Here we study polysemy as a potential learning bias in vocabulary learning in children. Words of low polysemy could be preferred as they reduce the disambiguation effort for the listener. However, such preference could be a side-effect of another bias: the preference of children for nouns in combination with the lower polysemy of nouns with respect to other part-of-speech categories.Our results show that mean polysemy in children increases over time in two phases, i.e. a fast growth till the 31st month followed by a slower tendency towards adult speech. In contrast, this evolution is not found in adults interacting with children. This suggests that children have a preference for non-polysemous words in their early stages of vocabulary acquisition. Interestingly, the evolutionary pattern described above weakens when controlling for syntactic category (noun, verb, adjective or adverb) but it does not disappear completely, suggesting that it could result from a combination of a standalone bias for low polysemy and a preference for nouns.

The pioneering research of G. K. Zipf on the relationship between word frequency and other word features led to the formulation of various linguistic laws. The most popular is Zipf's law for word frequencies. Here we focus on two laws that have been studied less intensively: the meaning-frequency law, i.e. the tendency of more frequent words to be more polysemous, and the law of abbreviation, i.e. the tendency of more frequent words to be shorter. In a previous work, we tested the robustness of these Zipfian laws for English, roughly measuring word length in number of characters and distinguishing adult from child speech. In the present article, we extend our study to other languages (Dutch and Spanish) and introduce two additional measures of length: syllabic length and phonemic length. Our correlation analysis indicates that both the meaning-frequency law and the law of abbreviation hold overall in all the analyzed languages.

In his pioneering research, G. K. Zipf formulated a couple of statistical laws on the relationship between the frequency of a word with its number of meanings: the law of meaning distribution, relating the frequency of a word and its frequency rank, and the meaning-frequency law, relating the frequency of a word with its number of meanings. Although these laws were formulated more than half a century ago, they have been only investigated in a few languages. Here we present the first study of these laws in Catalan. We verify these laws in Catalan via the relationship among their exponents and that of the rank-frequency law. We present a new protocol for the analysis of these Zipfian laws that can be extended to other languages. We report the first evidence of two marked regimes for these laws in written language and speech, paralleling the two regimes in Zipf’s rank-frequency law in large multi-author corpora discovered in early 2000s. Finally, the implications of these two regimes will be discussed.