Concept Extraction Challenge: University of Twente at #MSM2013

Twitter messages are a potentially rich source of continuously and instantly updated information. Shortness and informality of such messages are challenges for Natural Language Processing tasks. In this paper we present a hybrid approach for Named Entity Extraction (NEE) and Classification (NEC) for tweets. The system uses the power of the Conditional Random Fields (CRF) and the Support Vector Machines (SVM) in a hybrid way to achieve better results. For named entity type classification we used AIDA \cite{YosefHBSW11} disambiguation system to disambiguate the extracted named entities and hence find their type

Cardinal Virtues: Extracting Relation Cardinalities from Text

Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.Comment: 5 pages, ACL 2017 (short paper

Supervised Machine Learning Techniques to Detect TimeML Events in French and English

International audienceIdentifying events from texts is an information extraction task necessary for many NLP applications. Through the TimeML specifications and TempEval challenges, it has received some attention in the last years; yet, no reference result is available for French. In this paper, we try to fill this gap by proposing several event extraction systems, combining for instance Conditional Random Fields, language modeling and k-nearest-neighbors. These systems are evaluated on French corpora and compared with state-of-the-art methods on English. The very good results obtained on both languages validate our whole approach

A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance

The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finitestate conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets

Extracting Drug-Drug Interactions with Character-Level and Dependency-Based Embeddings

The DDI track of TAC-2018 challenge addresses the problem of an information retrieval of drug-drug interactions on structured product labeling documents with discontinuous and overlapping entities. In this paper, we present our participation for event extraction subtask (Task 1). We used a supervised long-short-term memory (LSTM) network with conditional random fields decoding (LSTM-CRF) approach with an automatic exploring of words and characters features. Additional dependency-based information was integrated into word embeddings to allow better word representation. Our system performed with above median score

A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research

EKSTRAKSI KATA KUNCI DENGAN MENGGUNAKAN CONDITIONAL RANDOM FIELDS

ABSTRAKSI: Berbagai informasi dalam jumlah besar dapat diperoleh dengan mudah dari Internet. Tetapi, tidak mudah untuk mencari informasi yang berguna diantara sekian banyak sumber informasi yang kita dapatkan. Beberapa search engine dapat melakukan pencarian teks lengkap, tetapi hasilnya kurang memuaskan. Efiesiensi pencarian dari jumlah dokumen yang sangat banyak sangat dipengaruhi oleh kualitas dari kata kunci yang diberikan oleh setiap dokumen. Oleh karena itu, perlu dibutuhkan kata kunci yang berkualitas yang diambil secara otomatis di-ekstrak dari dokumen-dokumen agar mendapatkan nilai efisiensi yang tinggi. Pada Tugas Akhir ini, akan membahas mengenai ”Ekstraksi Kata Kunci dengan menggunakan Conditional Random Fields”. Conditional Random Fields (CRF) adalah model probabilistik untuk segmentasi dan pelabelan data sekuens. CRF menggunakan teknik yang berbeda dalam melakukan preprocessing dimana kalimat-kalimat yang ada akan disegmentasi dan diberi label, sehingga akan memberikan pengaruh terhadap hasil dari ekstraksi kata kunci tersebut. Pengujian dilakukan dengan menghitung nilai precision, recall dan F-Measure untuk mengetahui performansi sistem yang akan dibuat. Hasil pengujian menunjukkan bahwa ekstraksi kata kunci dengan CRF memiliki akurasi yang lebih baik pada jenis dokumen yang bersifat umum (topik umum) atau dengan kata lain memiliki variasi data yang tinggi. Sedangkan dari sisi fungsi fiturnya, fitur POS (tanpa normalisasi) memberi pengaruh yang cukup signifikan terhadap performansi sistem, sedangkan fitur Len tidak memberikan pengaruh yang signifikan terhadap performansi sistem. Selain itu, berdasarkan jumlah dokumen trainingnya, ekstraksi kata kunci dengan CRF bisa diterapkan secara efektif walaupun jumlah data trainingnya sedikit.Kata Kunci : Ekstraksi kata kunci, Conditional Random Fields, preprocessing, fitur POS, fitur Len, F-Measure.ABSTRACT: A variety of large amounts of information can be obtained easily from the Internet. However, it is not easy to find useful information among the many sources of information that we get. Some search engines can perform full text search, but the results are less satisfactory. Efficiency search of the number of documents that are very much very much influenced by the quality of the keywords provided by each document. Therefore, the necessary quality required keywords are retrieved automatically extracted from documents in order to obtain high efficiency values. In this Final Project, will discuss "Keyword Extraction using Conditional Random Fields". Conditional Random Fields (CRF) are probabilistic models for segmenting and labeling sequence data. CRF using different techniques in performing preprocessing in which sentences are to be segmented and labeled, so that will give effect to the results of keyword extraction. Test results showed that the extraction of keywords with CRF have a better accuracy on the document type of a general nature (general topics), or in other words having high data variation. In terms of its functions, POS features (without normalization) gives significant effect on system performance, while Len feature does not have a significant influence on system performance. In addition, training was based on the number of documents, extraction of keywords with CRF can be applied effectively even though the amount of training was a bit of data.Keyword: Keyword extraction, Conditional Random Fields, preprocessing, POS feature, Len feature, F-Measure