Information extraction

In this paper we present a new approach to extract relevant information by knowledge graphs from natural language text. We give a multiple level model based on knowledge graphs for describing template information, and investigate the concept of partial structural parsing. Moreover, we point out that expansion of concepts plays an important role in thinking, so we study the expansion of knowledge graphs to use context information for reasoning and merging of templates

Effects of ecstasy/polydrug use on memory for associative information

Rationale Associative learning underpins behaviours that are fundamental to the everyday functioning of the individual. Evidence pointing to learning deficits in recreational drug users merits further examination. Objectives A word pair learning task was administered to examine associative learning processes in ecstasy/polydrug users. Methods After assignment to either single or divided attention conditions, 44 ecstasy/polydrug users and 48 non-users were presented with 80 word pairs at encoding. Following this, four types of stimuli were presented at the recognition phase: the words as originally paired (old pairs), previously presented words in different pairings (conjunction pairs), old words paired with new words, and pairs of new words (not presented previously). The task was to identify which of the stimuli were intact old pairs. Results Ecstasy/ploydrug users produced significantly more false-positive responses overall compared to non-users. Increased long-term frequency of ecstasy use was positively associated with the propensity to produce false-positive responses. It was also associated with a more liberal signal detection theory decision criterion value. Measures of long term and recent cannabis use were also associated with these same word pair learning outcome measures. Conjunction word pairs, irrespective of drug use, generated the highest level of false-positive responses and significantly more false-positive responses were made in the divided attention condition compared to the single attention condition. Conclusions Overall, the results suggest that long-term ecstasy exposure may induce a deficit in associative learning and this may be in part a consequence of users adopting a more liberal decision criterion value

Connecting numbers to discrete quantification: A step in the child’s construction of integer concepts

The present study asks when young children understand that number words quantify over sets of discrete individuals. For this study, 2- to 4-year-old children were asked to extend the number word five or six either to a cup containing discrete objects (e.g., blocks) or to a cup containing a continuous substance (e.g., water). In Experiment 1, only children who knew the exact meanings of the words one, two and three extended higher number words (five or six) to sets of discrete objects. In Experiment 2, children who only knew the exact meaning of one extended higher number words to discrete objects under the right conditions (i.e., when the problem was first presented with the number words one and two). These results show that children have some understanding that number words pertain to discrete quantification from very early on, but that this knowledge becomes more robust as children learn the exact, cardinal meanings of individual number words

From deep dyslexia to agrammatic comprehension on silent reading

We report on a case of a French-speaking patient whose performance on reading aloud single words was characteristically deep dyslexic (in spite of preserved ability to identify letters), and whose comprehension on silent sentence reading was agrammatic and strikingly poorer than on oral reading. The first part of the study is mainly informative as regards (i) the relationship between letter identification, semantic paralexias and the ability to read nonwords, (ii) the differential character of silent and oral reading tasks, and (iii) the potential modality-dependent character of the deficits in comprehension encountered. In the second part of the study we examine the patient's sensitivity to verb-noun ambiguity and probe her skills in the comprehension of indexical structures by exploring her ability to cope with number agreement and temporal and prepositional relations. The results indicate the patient's sensitivity to certain dimensions of these linguistic categories, reveal a partly correct basis for certain incorrect responses, and, on the whole, favor a definition of the patient's disorders in terms of a deficit in integrating indexical information in language comprehension. More generally, the present study substantiates a microgenetic approach to neuropsychology, where the pathological behavior due to brain damage is described as an arrest of microgenesis at an early stage of development, so that patient's responses take the form of unfinished "products" which would normally undergo further development

Vocabulary knowledge and reading

Includes bibliographical references (p. 35-43)Supported in part by the National Institute of Education under contract no. US-NIE-C-400-76-011

Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences

Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of disambiguating information. We evaluate WSD using selectional preferences acquired for English adjective—noun, subject, and direct object grammatical relationships with respect to a standard test corpus. The selectional preferences are specific to verb or adjective classes, rather than individual word forms, so they can be used to disambiguate the co-occurring adjectives and verbs, rather than just the nominal argument heads. We also investigate use of the one-senseper-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage. Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage. In addition to quantifying performance, we analyze the results to investigate the situations in which the selectional preferences achieve the best precision and in which the one-sense-per-discourse heuristic increases performance

Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Separating the influences of prereading skills on early word and nonword reading

The essential first step for a beginning reader is to learn to match printed forms to phonological representations. For a new word, this is an effortful process where each grapheme must be translated individually (serial decoding). The role of phonological awareness in developing a decoding strategy is well known. We examined whether beginning readers recruit different skills depending on the nature of the words being read (familiar words vs. nonwords). Print knowledge, phoneme and rhyme awareness, rapid automatized naming (RAN), phonological short-term memory (STM), nonverbal reasoning, vocabulary, auditory skills, and visual attention were measured in 392 prereaders 4 and 5 years of age. Word and nonword reading were measured 9 months later. We used structural equation modeling to examine the skills–reading relationship and modeled correlations between our two reading outcomes and among all prereading skills. We found that a broad range of skills were associated with reading outcomes: early print knowledge, phonological STM, phoneme awareness and RAN. Whereas all of these skills were directly predictive of nonword reading, early print knowledge was the only direct predictor of word reading. Our findings suggest that beginning readers draw most heavily on their existing print knowledge to read familiar words