Метод автоматической экстракции парадигматических отношений между понятиями толкового словаря

В статье рассматривается метод автоматического выявления семантических парадигматических отношений между концептами толкового словаря. Для выявления межконцептуальных отношений принадлежности к классу, гиперонимии, гипонимии и меронимии использованы шаблоны лексических последовательностей; для определения семантической эквивалентности вычисляется мера смысловой близости. Описана разработанная программная реализация метода и приводятся экспериментально определенные показатели качества работы.In this article, the method of the automatic identification of semantic paradigmatic relations between concepts of the glossary has been considered. Patterns of lexical sets have been used for the identification of concepts’ relations of belonging to the class, hypernymy, hyponymy and myronymy. A measure of the semantic proximity has been determined for the identification of the semantic equivalence. The developed software implementation has been described and the quality indexes experimentally determined have been produced

Метод автоматической экстракции парадигматических отношений между понятиями толкового словаря

В статье рассматривается метод автоматического выявления семантических парадигматических отношений между концептами толкового словаря. Для выявления межконцептуальных отношений принадлежности к классу, гиперонимии, гипонимии и меронимии использованы шаблоны лексических последовательностей; для определения семантической эквивалентности вычисляется мера смысловой близости. Описана разработанная программная реализация метода и приводятся экспериментально определенные показатели качества работы.In this article, the method of the automatic identification of semantic paradigmatic relations between concepts of the glossary has been considered. Patterns of lexical sets have been used for the identification of concepts’ relations of belonging to the class, hypernymy, hyponymy and myronymy. A measure of the semantic proximity has been determined for the identification of the semantic equivalence. The developed software implementation has been described and the quality indexes experimentally determined have been produced

Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification

We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset.Comment: EMNLP 201

Multilingual Extraction of functional relations between Arabic Named Entities using NooJ platform

10 pagesInternational audienceThe extraction of relation between Named Entities (NE) has become the last few years an interesting research domain. It is very useful for many applications such as Web mining, Information extraction and retrieval, Business intelligence, Automatic databases filing with Entities & types, Questions answering task and document Summarization. Several works has been performed for relation discovery in texts written in Latin languages and as far as we know, very few works has been done for Arabic language. In this paper, we focus on functional relations between ENAMEX and ORG Arabic Named Entities. The extraction approach is rule based and the implementation is performed using NooJ Platform

Crime Analysis Using Self Learning

An unsupervised algorithm for event extraction is proposed . Some small number of seed examples and corpus of text documents are used as inputs. Here, we are interested in finding out relationships which may be spanned over the entire length of the document. The goal is to extract relations among mention that lie across sentences. These mention relations can be binary, ternary or even quaternary relations. For this paper our algorithm concentrates on picking out a specific binary relation in a tagged data set. We are using co reference resolution to solve the problem of relation extraction. Earlier approaches co - refer identity relations while our approach co - refers independent mention pairs based on feature rules. This paper proposes an approach for coreference resolution which uses the EM (Expectation Maximization) algorithm as a reference to train data and co relate entities inter sentential

Entity Recognition at First Sight: Improving NER with Eye Movement Information

Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural model for named entity recognition (NER) with gaze embeddings. These corpora were manually annotated with named entity labels. Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time. The gaze-augmented models for NER using token-level and type-level features outperform the baselines. We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.Comment: Accepted at NAACL-HLT 201

GAMBARAN KASUS GIGI IMPAKSI DAN TINGKAT PENGETAHUAN PASIEN PENDERITA GIGI IMPAKSI DI RUMAH SAKIT ISLAM SULTAN AGUNG SEMARANG

Gigi impaksi merupakan pertumbuhan gigi yang tidak normal karena terhalang oleh gigi sebelah sehingga tumbuh tidak sesuai lengkung rahang. Tingginya jumlah penyakit pada lingkungan masyarakat masih sering ditemukan, diakibatkan rendahnya pengetahuan masyarakat. Penelitian ini bertujuan untuk mengetahui gambaran kasus gigi impaksi dan gambaran tingkat pengetahuan pasien penderita gigi impaksi di Rumah Sakit Islam Sultan Agung Semarang.Penelitian ini merupakan penelitian jenis deskriptif melibatkan 54 subjek pasien penderita gigi impaksi yang sudah tertera data rekam medis dan tingkat pengetahuan berdasarkan kuesioner yang dibagikan kepada pasien penderita gigi impaksi.Penelitian yang didapatkan adanya gambaran kasus gigi impaksi di Rumah Sakit Islam Sultan Agung Semarang pada gigi molar tiga mencapai 135 gigi impaksi (94,6%) dan gigi molar dua mencapai 3 gigi impaksi (5,6%). Hasil tingkat pengetahuan pasien penderita gigi impaksi untuk tingkatan rendah mencapai 6 orang (11,1%), tingkatan sedang mencapai 40 orang (74,1%) dan tingkatan tinggi mencapai 8 orang (14,8%).Penelitian ini adalah gigi impaksi paling banyak terjadi pada gigi molar tiga. Tingkat pengetahuan pasien gigi impaksi tergolong dalam tingkat sedang.Kata kunci : Gigi impaksi, Kesehatan gigi dan mulut, Tingkat pengetahua

Improving the extraction of complex regulatory events from scientific text by using ontology-based inference

<p>Abstract</p> <p>Background</p> <p>The extraction of complex events from biomedical text is a challenging task and requires in-depth semantic analysis. Previous approaches associate lexical and syntactic resources with ontologies for the semantic analysis, but fall short in testing the benefits from the use of domain knowledge.</p> <p>Results</p> <p>We developed a system that deduces implicit events from explicitly expressed events by using inference rules that encode domain knowledge. We evaluated the system with the inference module on three tasks: First, when tested against a corpus with manually annotated events, the inference module of our system contributes 53.2% of correct extractions, but does not cause any incorrect results. Second, the system overall reproduces 33.1% of the transcription regulatory events contained in RegulonDB (up to 85.0% precision) and the inference module is required for 93.8% of the reproduced events. Third, we applied the system with minimum adaptations to the identification of cell activity regulation events, confirming that the inference improves the performance of the system also on this task.</p> <p>Conclusions</p> <p>Our research shows that the inference based on domain knowledge plays a significant role in extracting complex events from text. This approach has great potential in recognizing the complex concepts of such biomedical ontologies as Gene Ontology in the literature.</p

Advancing NLP with Cognitive Language Processing Signals

When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks. We analyze whether using such human features can show consistent improvement across tasks and data sources. We present an extensive investigation of the benefits and limitations of using cognitive processing data for NLP. Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. These methods significantly outperform the baselines and show the potential and current limitations of employing human language processing data for NLP