2012 EnsemblebasedSemanticLexiconInd

• (Qadir & Riloff, 2012) ⇒ Ashequl Qadir, and Ellen Riloff. (2012). “Ensemble-based Semantic Lexicon Induction for Semantic Tagging.” In: Proceedings of the First Joint Conference on Lexical and Computational Semantics (*Sem 2012).

Subject Headings: Semantic Lexicon, Ensemble-based Sequence Chunking Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%22Ensemble-based+semantic+lexicon+induction+for+semantic+tagging%22+2012
• http://dl.acm.org/citation.cfm?id=2387636.2387669&preflayout=flat#citedby

Quotes

Abstract

We present an ensemble-based framework for semantic lexicon induction that incorporates three diverse approaches for semantic class identification. Our architecture brings together previous bootstrapping methods for pattern-based semantic lexicon induction and contextual semantic tagging, and incorporates a novel approach for inducing semantic classes from coreference chains. The three methods are embedded in a bootstrapping architecture where they produce independent hypotheses, consensus words are added to the lexicon, and the process repeats. Our results show that the ensemble outperforms individual methods in terms of both lexicon quality and instance-based semantic tagging.

1. Introduction

One of the most fundamental aspects of meaning is the association between words and semantic categories, which allows us to understand that a “cow” is an animal and a “house” is a structure. We will use the term semantic lexicon to refer to a dictionary that associates words with semantic classes. Semantic dictionaries are useful for many NLP tasks, as evidenced by the widespread use of WordNet (Miller, 1990). However, off-the-shelf resources are not always sufficient for specialized domains, such as medicine, chemistry, or microelectronics. Furthermore, in virtually every domain, texts contain lexical variations that are often missing from dictionaries, such as acronyms, abbreviations, spelling variants, informal shorthand terms (e.g., “abx” for “antibiotics”), and composite terms (e.g., “may-december” or “virus/worm”). To address this problem, techniques have been developed to automate the construction of semantic lexicons from text corpora using bootstrapping methods (Riloff and Shepherd, 1997; Roark and Charniak, 1998; Phillips and Riloff, 2002; Thelen and Riloff, 2002; Ng, 2007; McIntosh and Curran, 2009; McIntosh, 2010), but accuracy is still far from perfect.

Our research explores the use of ensemble methods to improve the accuracy of semantic lexicon induction. Our observation is that semantic class associations can be learned using several fundamentally different types of corpus analysis. Bootstrapping methods for semantic lexicon induction (e.g., (Riloff and Jones, 1999; Thelen and Riloff, 2002; McIntosh and Curran, 2009)) collect corpus-wide statistics for individual words based on shared contextual patterns. In contrast, classifiers for semantic tagging (e.g., (Collins and Singer, 1999; Niu et al., 2003; Huang and Riloff, 2010)) label word instances and focus on the local context surrounding each instance. The difference between these approaches is that semantic taggers make decisions based on a single context and can assign different labels to different instances, whereas lexicon induction algorithms compile corpus statistics from multiple instances of a word and typically assign each word to a single semantic category. We also hypothesize that coreference resolution can be exploited to infer semantic class labels. Intuitively, if we know that two noun phrases are coreferent, then they probably belong to the same high-level semantic category (e.g., “dog” and “terrier” are both animals).

In this paper, we present an ensemble-based framework for semantic lexicon induction. We incorporate a pattern-based bootstrapping method for lexicon induction, a contextual semantic tagger, and a new coreference-based method for lexicon induction. Our results show that coalescing the decisions produced by diverse methods produces a better dictionary than any individual method alone.

A second contribution of this paper is an analysis of the effectiveness of dictionaries for semantic tagging. In principle, an NLP system should be able to assign different semantic labels to different senses of a word. But within a specialized domain, most words have a dominant sense and we argue that using domain-specific dictionaries for tagging may be equally, if not more, effective. We analyze the tradeoffs between using an instance-based semantic tagger versus dictionary lookup on a collection of disease outbreak articles. Our results show that the induced dictionaries yield better performance than an instance-based semantic tagger, achieving higher accuracy with comparable levels of recall.

2 Related Work

Several techniques have been developed for semantic class induction (also called set expansion) using bootstrapping methods that consider co-occurrence statistics based on nouns (Riloff and Shepherd, 1997), syntactic structures (Roark and Charniak, 1998; Phillips and Riloff, 2002), and contextual patterns (Riloff and Jones, 1999; Thelen and Riloff, 2002; McIntosh and Curran, 2008; McIntosh and Curran, 2009). To improve the accuracy of induced lexicons, some research has incorporated negative information from human judgements (Vyas and Pantel, 2009), automatically discovered negative classes (McIntosh, 2010), and distributional similarity metrics to recognize concept drift (McIntosh and Curran, 2009). Phillips and Riloff (2002) used co-training (Blum and Mitchell, 1998) to exploit three simple classifiers that each recognized a different type of syntactic structure. The research most closely related to ours is an ensemble-based method for automatic thesaurus construction (Curran, 2002). However, that goal was to acquire finegrained semantic information that is more akin to synonymy (e.g., words similar to “house”), whereas we associate words with high-level semantic classes (e.g., a “house” is a transient structure).

Semantic class tagging is closely related to named entity recognition (NER) (e.g., (Bikel et al., 1997; Collins and Singer, 1999; Cucerzan and Yarowsky, 1999; Fleischman and Hovy, 2002)). Some bootstrapping methods have been used for NER (e.g., (Collins and Singer, 1999; Niu et al., 2003) to learn from unannotated texts. However, most NER systems will not label nominal noun phrases (e.g., they will not identify “the dentist” as a person) or recognize semantic classes that are not associated with proper named entities (e.g., symptoms). ACE mention detection systems (e.g., (ACE, 2007; ACE, 2008)) can label noun phrases that are associated with 5-7 semantic classes and are typically trained with supervised learning. Recently, (Huang and Riloff, 2010) developed a bootstrapping technique that induces a semantic tagger from unannotated texts. We use their system in our ensemble. There has also been work on extracting semantic class members from theWeb (e.g., (Pas¸ca, 2004; Etzioni et al., 2005; Kozareva et al., 2008; Carlson et al., 2009)). This line of research is fundamentally different from ours because these techniques benefit from the vast repository of information available on theWeb and are therefore designed to harvest a wide swath of general-purpose semantic information. Our research is aimed at acquiring domain-specific semantic dictionaries using a collection of documents representing a specialized domain.

3 Ensemble-based Semantic Lexicon Induction

3.1 Motivation

Our research combines three fundamentally different techniques into an ensemble-based bootstrapping framework for semantic lexicon induction: pattern-based dictionary induction, contextual semantic tagging, and coreference resolution. Our motivation for using an ensemble of different techniques is driven by the observation that these methods exploit different types of information to infer semantic class knowledge. The coreference resolver uses features associated with coreference, such as syntactic constructions (e.g., appositives, predicate nominals), word overlap, semantic similarity, proximity, etc. The pattern-based lexicon induction algorithm uses corpus-wide statistics gathered from the contexts of all instances of a word and compares them with the contexts of known category members. The contextual semantic tagger uses local context windows around words and classifies each word instance independently from the others.

Since each technique draws its conclusions from different types of information, they represent independent sources of evidence to confirm whether a word belongs to a semantic class. Our hypothesis is that, combining these different sources of evidence in an ensemble-based learning framework should produce better accuracy than using any one method alone. Based on this intuition, we create an ensemble-based bootstrapping framework that iteratively collects the hypotheses produced by each individual learner and selects the words that were hypothesized by at least 2 of the 3 learners. This approach produces a bootstrapping process with improved precision, both at the critical beginning stages of the bootstrapping process and during subsequent bootstrapping iterations.

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4.4 Dictionary Evaluation

To assess the quality of the lexicons, we estimated their accuracy by compiling external word lists from freely available sources such as Wikipedia and WordNet (Miller, 1990). Table 1 shows the sources that we used, where the bracketed items refer to WordNet hypernym categories. We searched each WordNet hypernym tree (also, instancerelationship) for all senses of the word. Additionally, we collected the manually labeled words in our test set and included them in our gold standard lists. Since the induced lexicons contain individual nouns, we extracted only the head nouns of multiword phrases in the external resources. This can produce incorrect entries for non-compositional phrases, but we found this issue to be relatively rare and we manually removed obviously wrong entries.

We adopted a conservative strategy and assumed that any lexicon entries not present in our gold standard lists are incorrect. But we observed many correct entries that were missing from the external resources, so our results should be interpreted as a lower bound on the true accuracy of the induced lexicons. We generated lexicons for each method separately, and also for the ensemble and co-training models. We ran Basilisk for 100 iterations (500 words). We refer to a Basilisk lexicon of size N using the notation B[N]. For example, B400 refers to a lexicon containing 400 words, which was generated from 80 bootstrapping cycles. We refer to the lexicon obtained from the semantic tagger as ST Lex.

Figure 2 shows the dictionary evaluation results. We plotted Basilisk’s accuracy after every 5 bootstrapping cycles (25 words). For ST Lex, we sorted the words by their confidence scores and plotted the accuracy of the top-ranked words in increments of 50. The plots for Coref, Co-Training, and Ensemble B[N] are based on the lexicons produced after each bootstrapping cycle.

The ensemble-based framework yields consistently better accuracy than the individual methods for Animal, Body Part, Human and Temporal Reference, and similar if not better for Disease & Symptom, Fixed Location, Organization, Plant & Food. However, relying on consensus from multiple models produce smaller dictionaries. Big dictionaries are not always better than small dictionaries in practice, though. We believe, it matters more whether a dictionary contains the most frequent words for a domain, because they account for a disproportionate number of instances. Basilisk, for example, often learns infrequent words, so its dictionaries may have high accuracy but often fail to recognize common words. We investigate this issue in the next section.

4.5 Instance-based Tagging Evaluation

We also evaluated the effectiveness of the induced lexicons with respect to instance-based semantic tagging. Our goal was to determine how useful the dictionaries are in two respects: (1) do the lexicons contain words that appear frequently in the domain, and (2) is dictionary look-up sufficient for instance-based labeling? Our bootstrapping processes enforce a constraint that a word can only belong to one semantic class, so if polysemy is common, then dictionary look-up will be problematic.

The instance-based evaluation assigns a semantic label to each instance of a head noun. When using a lexicon, all instances of the same noun are assigned the same semantic class via dictionary look-up. The semantic tagger (SemTag), however, is applied directly since it was designed to label instances.

Table 2 presents the results. As a baseline, the W.Net row shows the performance of WordNet for instance tagging. For words with multiple senses, we only used the first sense listed in WordNet. The Seeds row shows the results when performing dictionary look-up using only the seed words. The remaining rows show the results for Basilisk (B100 and B400), coreference-based lexicon induction (Coref), lexicon induction using the semantic tagger (ST Lex), and the original instance-based tagger (SemTag). The following rows show the results for co-training (after 4 iterations and 20 iterations) and for the ensemble (using Basilisk size 100 and size 400). Table 3 shows the micro & macro average results across all semantic categories.

Figure 2: Dictionary Evaluation Results

Table 3 shows that the dictionaries produced by the Ensemble w/B100 achieved better results than the individual methods and co-training with an F score of 80%. Table 2 shows that the ensemble achieved better performance than the other methods for 4 of the 9 classes, and was usually competitive on the remaining 5 classes. WordNet (W.Net) consistently produced high precision, but with comparatively lower recall, indicating that WordNet does not have sufficient coverage for this domain.

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- Method Micro Average Macro Average P R F P R F Ensemble with component pairs ST Lex+Coref 92 59 72 92 57 70 B100+Coref 92 40 56 94 44 60 ST Lex+B100 82 69 75 81 75 77 Ensemble with all components ST Lex+B100+Coref 83 77 80 81 80 80 Table 4: Ablation Study of the Ensemble Framework for Semantic Tagging

5 Conclusions

Our research combined three diverse methods for semantic lexicon induction in a bootstrapped ensemble-based framework, including a novel approach for lexicon induction based on coreference chains. Our ensemble-based approach performed better than the individual methods, in terms of both dictionary accuracy and instance-based semantic tagging. In future work, we believe this approach could be enhanced further by adding new types of techniques to the ensemble and by investigating better methods for estimating the confidence scores from the individual components.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 EnsemblebasedSemanticLexiconInd	Ellen Riloff Ashequl Qadir			Ensemble-based Semantic Lexicon Induction for Semantic Tagging