A preliminary study in zero anaphora coreference resolution for Polish

A preliminary study in zero anaphora coreference resolution for PolishZero anaphora is an element of the coreference resolution task that has not yet been directly addressed in Polish and, in most studies, it has been left as the most challenging aspect for further investigation. This article presents an initial study of this problem. The preparation of a machine learning approach, alongside engineering features based on linguistic study of the KPWr corpus, is discussed. This study utilizes existing tools for Polish coreference resolution as sources of partial coreferential clusters containing pronoun, noun and named entity mentions. They are also used as baseline zero coreference resolution systems for comparison with our system. The evaluation process is focused not only on clustering correctness, without taking into account types of mentions, using standard CoNLL-2012 measures, but also on the informativeness of the resulting relations. According to the annotation approach used for coreference to the KPWr corpus, only named entities are treated as mentions that are informative enough to constitute a link to real world objects. Consequently, we provide an evaluation of informativeness based on found links between zero anaphoras and named entities. For the same reason, we restrict coreference resolution in this study to mention clusters built around named entities. Wstępne studium rozwiązywania problemu koreferencji anafory zerowej w języku polskimKoreferencja zerowa, w języku polskim, jest jednym z zagadnień rozpoznawania koreferencji. Dotychczas nie była ona bezpośrednim przedmiotem badań, gdyż ze względu na jej złożoność była pomijana i odsuwana na dalsze etapy badań. Artykuł prezentuje wstępne studium problemu, jakim jest rozpoznawanie koreferencji zerowej. Przedstawiamy podejście wykorzystujące techniki uczenia maszynowego oraz proces tworzenia cech w oparciu o analizę lingwistyczną korpusu KPWr. W przedstawionej pracy wykorzystujemy istniejące narzędzia do rozpoznawania koreferencji dla pozostałych rodzajów wzmianek (tj. nazwy własne, frazy rzeczownikowe oraz zaimki) jako źródło częściowych zbiorów wzmianek odnoszących się do tego samego obiektu, a także jako punkt odniesienia dla uzyskanych przez nas wyników. Ocena skupia się nie tylko na poprawności uzyskanych zbiorów wzmianek, bez względu na ich typ, co odzwierciedlają wyniki podane dla standardowych metryk CoNLL-2012, ale także na wartości informacji, która zostaje uzyskana w wyniku rozpoznania koreferencji. W nawiązaniu do założeń anotacji korpusu KPWr, jedynie nazwy własne traktowane są jako wzmianki, które zawierają w sobie wystarczająco szczegółową informację, aby można było powiązać je z obiektami rzeczywistymi. W konsekwencji dostarczamy także ocenę opartą na wartości informacji dla podmiotów domyślnych połączonych relacją koreferencji z nazwami własnymi. Z tą samą motywacją rozpatrujemy jedynie zbiory wzmianek koreferencyjnych zbudowane wokół nazw własnych.

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Teaching Machines to Find Names

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch229 ◽

2011 ◽

pp. 1562-1567

Author(s):

Raymond Chiong

Keyword(s):

Machine Learning ◽

Statistical Models ◽

Learning Approach ◽

Named Entities ◽

Rule Based ◽

Named Entity ◽

Machine Learning Approach ◽

Wide Availability ◽

Increasing Demand ◽

Rule Based Systems

In the field of Natural Language Processing, one of the very important research areas of Information Extraction (IE) comes in Named Entity Recognition (NER). NER is a subtask of IE that seeks to identify and classify the predefined categories of named entities in text documents. Considerable amount of work has been done on NER in recent years due to the increasing demand of automated texts and the wide availability of electronic corpora. While it is relatively easy and natural for a human reader to read and understand the context of a given article, getting a machine to understand and differentiate between words is a big challenge. For instance, the word ‘brown’ may refer to a person called Mr. Brown, or the colour of an item which is brown. Human readers can easily discern the meaning of the word by looking at the context of that particular sentence, but it would be almost impossible for a computer to interpret it without any additional information. To deal with the issue, researchers in NER field have proposed various rule-based systems (Wakao, Gaizauskas & Wilks, 1996; Krupka & Hausman, 1998; Maynard, Tablan, Ursu, Cunningham & Wilks, 2001). These systems are able to achieve high accuracy in recognition with the help of some lists of known named entities called gazetteers. The problem with rule-based approach is that it lacks the robustness and portability. It incurs steep maintenance cost especially when new rules need to be introduced for some new information or new domains. A better option is thus to use machine learning approach that is trainable and adaptable. Three wellknown machine learning approaches that have been used extensively in NER are Hidden Markov Model (HMM), Maximum Entropy Model (MEM) and Decision Tree. Many of the existing machine learning-based NER systems (Bikel, Schwartz & Weischedel, 1999; Zhou & Su, 2002; Borthwick, Sterling, Agichten & Grisham, 1998; Bender, Och & Ney, 2003; Chieu & Ng, 2002; Sekine, Grisham & Shinnou, 1998) are able to achieve near-human performance for named entity tagging, even though the overall performance is still about 2% short from the rule-based systems. There have also been many attempts to improve the performance of NER using a hybrid approach with the combination of handcrafted rules and statistical models (Mikheev, Moens & Grover, 1999; Srihari & Li, 2000; Seon, Ko, Kim & Seo, 2001). These systems can achieve relatively good performance in the targeted domains owing to the comprehensive handcrafted rules. Nevertheless, the portability problem still remains unsolved when it comes to dealing with NER in various domains. As such, this article presents a hybrid machine learning approach using MEM and HMM successively. The reason for using two statistical models in succession instead of one is due to the distinctive nature of the two models. HMM is able to achieve better performance than any other statistical models, and is generally regarded as the most successful one in machine learning approach. However, it suffers from sparseness problem, which means considerable amount of data is needed for it to achieve acceptable performance. On the other hand, MEM is able to maintain reasonable performance even when there is little data available for training purpose. The idea is therefore to walkthrough the testing corpus using MEM first in order to generate a temporary tagging result, while this procedure can be simultaneously used as a training process for HMM. During the second walkthrough, the corpus uses HMM for the final tagging. In this process, the temporary tagging result generated by MEM will be used as a reference for subsequent error checking and correction. In the case when there is little training data available, the final result can still be reliable based on the contribution of the initial MEM tagging result.

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Coreference resolution of Korean anaphoric zero objects: Towards a supervised machine learning approach

International Journal of Computer Science and Information Technology for Education ◽

10.21742/ijcsite.2016.1.01 ◽

2016 ◽

Vol 1 (1) ◽

pp. 1-6

Author(s):

Euhee Kim ◽

◽

Myung-Kwan Park ◽

Keyword(s):

Machine Learning ◽

Supervised Machine Learning ◽

Learning Approach ◽

Coreference Resolution ◽

Machine Learning Approach

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A Machine Learning Approach to Coreference Resolution of Noun Phrases

Computational Linguistics ◽

10.1162/089120101753342653 ◽

2001 ◽

Vol 27 (4) ◽

pp. 521-544 ◽

Cited By ~ 287

Author(s):

Wee Meng Soon ◽

Hwee Tou Ng ◽

Daniel Chung Yong Lim

Keyword(s):

Machine Learning ◽

Noun Phrase ◽

State Of The Art ◽

Noun Phrases ◽

Learning Approach ◽

Data Sets ◽

Coreference Resolution ◽

Machine Learning Approach

In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just a certain type of noun phrase (e.g., pronouns) but rather general noun phrases. It also does not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of “organization,” “person,” or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, indicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Our system is the first learning-based system that offers performance comparable to that of state-of-the-art nonlearning systems on these data sets.

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Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text

Cognitive Analytics ◽

10.4018/978-1-7998-2460-2.ch076 ◽

2020 ◽

pp. 1496-1512

Author(s):

Usha B. Biradar ◽

Harsha Gurulingappa ◽

Lokanath Khamari ◽

Shashikala Giriyan

Keyword(s):

Machine Learning ◽

System Performance ◽

Conditional Random Fields ◽

Learning Approach ◽

Named Entities ◽

Machine Learning Approach ◽

Feature Optimization ◽

Multi Level ◽

Corpus Generation ◽

Substantial Effort

Identification of chemical named entities in text and subsequent linkage of information to biological events is of immense value to fulfill the knowledge needs of pharmaceutical and chemical R&D. A significant amount of investigation has been carried out since a decade for identifying chemical named entities at morphological level. However, a barrier still remains in terms of value proposition to scientists at chemistry level. Therefore, the work described here aims to circumvent the information barrier by adaptation of a Conditional Random Fields-based approach for identifying chemical named entities at various levels namely generic chemical level, morphological level, and chemistry level. Substantial effort has been invested on generation of suitable multi-level annotated corpora. Recommended machine learning practices such as active learning-based training corpus generation and feature optimization have been systematically performed. Evaluation of system performance and benchmarking against the other state-of-the-approaches showed improved results.

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