Knowledge-Based Approaches

The main knowledge-based methods which utilise knowledge resources when disambiguating the senses of words in text include: the methods of overlap of sense definitions and the structural approaches (Manning and Schütze, 1999; Mihalcea, 2006). These approaches are described in more detail in Sections 2.2.4.4.1 and 2.2.4.4.2 respectively.

2.2.4.4.1 Overlap of Sense Definitions

The most well-known knowledge-based approach for WSD is the Lesk approach (Lesk, 1986), which is also used as a good baseline method in the evaluation of performance of the other WSD systems. This approach is based on the overlap strategy which finds the shared vocabulary between the words’ definitions (bag of words). It computes the overlaps (i.e. number of words in common) between the sense definitions of a target word and the other sense definitions of words in the

neighbourhood (context). The sense of the target word whose definition has the largest number of words in common (overlaps) is chosen to be the right sense of that target word.

The approach introduced by Banerjee and Pedersen (2003) presents a measure for semantic relatedness between concepts based on the use of the Lesk algorithm which has been adapted to WordNet. The idea is to extend the comparison between the sense definitions (glosses) of words to include the definitions of concepts (relations) that are related according to the hierarchies of concepts in WordNet (e.g. hypernyms, hyponyms, holonym etc). The authors have evaluated their approach on the Senseval- 2 data and the algorithm achieved an overall accuracy of 34.6%. They demonstrated that the extended Lesk algorithm has achieved better disambiguation accuracy than the original Lesk algorithm (the achieved accuracy by the original Lesk algorithm was 18.3%). Vasilescu et al. (2004) implement different variations of the Lesk algorithm which have been adapted to the WordNet for WSD. They demonstrated that the strategy of an algorithm variation depends on counting the overlaps between the bag of words (BOW) of a target word to be disambiguated and the BOW of its context, where the selected sense of the target word is the one that has the highest number of overlaps. The variation approaches have been evaluated on the Senseval-2 data and the results show that these variants outperform the original Lesk algorithm. The best precision rate5 _{that has been achieved by the implemented disambiguation approaches}

is 58%. A new study presented by Zouaghi, Merhbene and Zrigui (2012) develops a system that combines some information retrieval measures with the Lesk algorithm for Arabic WSD. The experimental evaluations of their system have shown an accuracy of 78% on collected corpora from the web (e.g. Wikipedia, Quran Arabic Corpus etc).

There are some open source systems which have been implemented to solve the ambiguity of words in text by specifying the correct sense of each target word in context. One of these systems is the free available Perl package called WordNet::SenseRelate::AllWords (SR-AW) (Pedersen and Kolhatkar, 2009). This system utilises different semantic relatedness measures including the extended ‘lesk’

5 _{Precision is a statistical classification function for evaluating the performance of a system. See}

measure (extended gloss overlaps) (Banerjee and Pedersen 2003) which is one of the main knowledge-based approaches for WSD. The SR-AW software assigns senses to all words in text by using information from a sense inventory provided by the WordNet lexical database.

The web interface of SR-AW enables the user to enter a sentence or an input file containing a text to be processed and disambiguated. The text in the input file can be untagged (raw) or tagged either with WordNet tags or with the Penn Treebank POS tags (Santorini, 1990). The SR-AW software applies ten measures to estimate the similarity and relatedness between words. The similarity measures use the hierarchical information available in WordNet to compare two words (either noun to noun or verb to verb). Some of these similarity measures include the ‘lch’ measure

(Leacock and Chodorow, 1998) and ‘jcn’ measure (Jiang and Conrath, 1997). The

‘lch’ measure is based on finding the shortest path between two concepts based on the

hierarchies of concepts in WordNet, while the ‘jcn’ measure combines the

information content values of two concepts with the information content of individual concepts to estimate values that specify the semantic distance between words.

On the other hand, the semantic relatedness measures include the adapted ‘lesk’

measure (Banerjee and Pedersen, 2003), context vectors (Patwardhan, 2003), and the ‘hso’ (Hirst and St-Onge) measure (Hirst and St-Onge, 1998). The context vectors compare sense definitions of words to create a vector space model for measuring relatedness between words, while the ‘lesk’ measure uses gloss overlaps for string

matching. The ‘hso’ measure is based on estimating lexical chains and finding paths between concepts in order to distinguish the concepts that are semantically related (see structural approaches in Section 2.2.4.4.2).

It is possible for the user of the SR-AW software to provide a stoplist file or use the default one. This file contains regular expressions, where any word in the text that matches one of these regular expressions is removed. Moreover, the SR-AW algorithm allows the user to fix the senses and choose a value for the disambiguation scheme. The user has also the option to control the size of the context window. The context window specifies which words are involved in estimating the relatedness between words. Generally, a context window of size N consists of the target word

which is the word in the centre of the context window (centre word) and then expands out to the left and right of the target word for N/2 content words. By utilising the SR- AW disambiguation algorithm, all possible senses of the target word to be disambiguated are calculated for relatedness to the possible senses of the surrounding words (context words) in the context window. Then, the sense of the target word that has the highest score for relatedness to the senses of the context words is considered to be the correct sense of that target word (Michelizzi, 2005). The score of a sense si is

computed as follows:





_s



where sjkis the k-th sense of j-th word around the target word. The result of SR-AW

depends on the selected relatedness measure and the size of context window.

After the SR-AW algorithm disambiguates the senses of words in context, it generates the output. The output displays the words that appear in the given text with the WordNet sense tags assigned. The WordNet sense tags include the word, the POS tag, the sense number and the sense definition for each of the assigned sense.

The performance of the SR-AW algorithm has been evaluated by Pedersen and Kolhatkar (2009). Their experiments were performed using three corpora (SemCor, SENSEVAL-2 and SENSEVAL-3) which have been manually tagged with senses from WordNet. They have used three sizes of the context window (2, 5 and 15) and three relatedness measures (lch, jcn and lesk) for the three corpora in the experiments. The results show that the disambiguation process using the ‘lesk’ measure provides

the highest precision and recall6 _{for the three corpora. The estimated f-measure rate is}

61% for SemCor, 59% for SENSEVAL-2 and 54% for SENSEVAL-3.

The SR-AW software has been utilised successfully in different problems and studies for the task of WSD. For example, the study proposed by Klyuev and Oleshchuk (2010) demonstrates a task for semantic query expansions. Their study utilised the SR-AW system and the WordNet lexicon to detect the meanings of the keywords of

the user query and check the semantic similarity between the user queries and the retrieved sentences of documents. In the approach presented by Shabanzadeh et al. (2010) for query expansion, the SR-AW software is also employed for disambiguating the query terms entered by a user. They evaluated their approach by computing the precision and recall rates, and then comparing them with keyword based information retrieval. Their experimental evaluations have achieved promising results.

2.2.4.4.2 Structural Approaches

Another kind of the knowledge-based methods is the structural approaches. These approaches exploit the structure of concepts available in computational lexicons such as WordNet to classify data based on the structural relations of features. Some approaches of this type are the similarity-based and graph-based techniques (Agirre and Edmonds, 2006; Navigli, 2009).

The similarity-based approaches apply several semantic similarity measures to estimate the similarity between concepts (or word senses) based on information available in a semantic lexicon such as WordNet. As WordNet organises the words into hierarchal relations of concepts, the measures of semantic similarity are used to compare the similarity between pair of concepts (or word senses) by estimating the shortest semantic distance (path) between them in the WordNet hierarchical structure. Many of the introduced similarity measures (e.g. the ‘lch’ measure (Leacock and

Chodorow, 1998) and the ‘jcn’ measure (Jiang and Conrath, 1997)) have been

implemented in the freely available Perl package called WordNet::Similarity (Pedersen et al. 2004) to compute the semantic similarity between words. The implemented modules of these measures take two concepts as input, and retrieve a numeric value which indicates the degree of similarity between these concepts. The WordNet::Similarity package utilises the WordNet::QueryData (Rennie, 2000) to access the WordNet database for creating objects.

The WordNet::QueryData (Rennie, 2000) module is a Perl interface that provides a direct access to the WordNet database files. It has the ability to retrieve semantic (e.g. hypernyms, hyponyms, domain categories etc) and lexical (e.g. antonym, verb group etc) relations from WordNet by using a set of functions (discussed later in more detail in Chapter 4). This Perl module has been applied successfully in many studies. For

instance, Wubben (2010) suggests an approach for ranking a set of verbs paraphrasing noun compounds by using features from WordNet and the Google N-gram corpus. In particular, his study applies WordNet::QueryData to determine the kind of semantic relations between noun compounds. Furthermore, the developed question answering system by Whidden (2005) uses WordNet::QueryData to access the WordNet database for processing relations and entities in order to retrieve semantic relations (e.g. hypernyms) that help in assigning a category for each question (expression).

With regards to the graph-based techniques, there are many approaches that are built on the use of the graph-based strategy for disambiguating the senses of words in context. The approaches of this type are mainly inspired by the notion of lexical chain (Morris and Hirst, 1991). The lexical chain is used to determine the context and the semantic relations (e.g. is-part-of relation, is-a kind of relation) between semantically related words (e.g. vehicle → car). Some researchers have applied different algorithms for computing the lexical chains between words in order to determine the semantic relations between them (e.g. Hirst and St-Onge, 1998).