Literature Survey
2.2 Approaches Resulting in Knowledge-poor Representa- Representa-tions
Approaches that employ techniques from standard Information Retrieval (IR) typically result in knowledge-poor representations. These approaches are adequate for retrieval oriented systems where the search for documents, information in documents, text or data is carried out, but the interpretation and understanding of the retrieved documents is the responsibility of the user. A document is treated as a bag-of-words (BOW) whereby text is broken up into individual tokens and word order is not taken into account. Terms which discriminate documents are then determined by ranking the terms using a frequency-based technique. The most popular ranking mechanisms are frequency-based on the Vector Space
2.2. Approaches Resulting in Knowledge-poor Representations 25 Model (Salton & McGill 1986) while other techniques like the Chi-square statistic may also be employed.
2.2.1 The Vector Space Model
Documents are represented by the words they contain. Terms will have different levels of importance in a document and over the entire document collection. Consequently, each term is associated with a weight that reflects these importances (Salton & Buckley 1988) hence the emergence of a weighting scheme. A weighting scheme will typically have three components; a local weight, a global weight and a normalisation factor. Thus, the weight wij of a term i in document j will typically be given by:
wij = lij× gi× nj (2.1)
where lij is the term’s local weight in the document j, gi is its global weight and nj is the normalisation factor for the jth document in the collection (Kokiopoulou & Saad 2004).
A vocabulary of the terms in the document collection is created. Each document j, is then represented as a vector based on the vocabulary where the non-zero elements are the document’s term-frequencies or TF(i,j). However, in order to reduce on computational costs, stopwords such as the, of and to are removed from the text as they are so common and therefore, not considered to convey useful information. Words are also reduced to their stems or lemma. Stemming refers to stripping a word of its suffix and lemmatisation is assigning a word to its grammatical base word form. This enables the different forms of a word to be regarded and thus treated the same. A term is likely to be important in a document if it is frequent in the document but infrequent in the other documents in the corpus. Putting this into consideration gives rare terms a higher chance of getting identified as relevant to the documents in which they appear. This is done by using the Inverse Document Frequency (IDF ) of a term i and is given by:
IDF (i) =−log(n
N) (2.2)
Where N is the number of documents in the corpus and n is the number of documents that
2.2. Approaches Resulting in Knowledge-poor Representations 26 contain term i. When the IDF value is multiplied with the term frequency TF(i,j), the Term Frequency Inverse Document Frequency (TFIDF ) weighting of a term i in document j is obtained:
T F IDF (i, j) = T F (i, j)× IDF (i) (2.3) Thus, according to Equation 2.1, the TFIDF weighting comprises a local weighting (TF) and a global weighting (IDF) but does not incorporate a normalisation factor. A term will be identified as important if it occurs frequently in some document but infrequently in other documents. Although TFIDF is used in the Vector Space Model as a term selection metric, it or its variants can be used in isolation to rank terms. However, none of these metrics incorporate semantics and thus it is possible to rank as low, infrequently occurring but important terms.
2.2.2 Apache Lucene
Apache Lucene1is a tool that is widely used in the Information Retrieval (IR) community.
Lucene makes use of a combination of the Vector Space Model (Salton & McGill 1986) and the Boolean model (Norreault, Koll & McGill 1977, Salton, Fox & Wu 1983) to determine a document’s relevance to a given query. In response to a query q Lucene assigns a score to each document d according to equation 2.4.
Score(d, q) =
t∈q
(tf (t, d).idf (t2).boost(t, q).boost(t, d).norm(d)) (2.4)
where
• tf(t, d) is the term frequency factor for the term t in the document d.
• idf(t) is the inverse document frequency of the term.
• boost(t.field, q) is the boost of the field of t in query q.
• boost(t.field, d) is the boost of the field of t in d, as set during indexing.
1http://lucene.apache.org
2.2. Approaches Resulting in Knowledge-poor Representations 27
• norm(d) is the normalisation value which depends on the number of terms in the document.
Thus, Lucene is quite sophisticated in its own right as it makes use of a combination of parameters to obtain the final ranking for a document. However, it treats each document as a bag-of-words and does not take word order or semantics into account.
2.2.3 The Chi-square Statistic
Another common approach to identifying key terms is to make use of the chi-square statis-tic. The one presented by Matsuo & Ishizuka (2004) measures the degree of association between all terms and the most frequent terms in a document. The frequent terms are used as anchor terms to extract keywords from the rest of the text. The probability of occurrence of each anchor term, normalised so that the sum is 1, is obtained. This is the relative frequency or unconditional probability of the anchor term. A term w that appears independently from a set of anchor terms, has its distribution of co-occurrence with the anchor terms similar to their unconditional probabilities. Conversely, if this term has a semantic relation with a particular set of anchor terms g∈ G, co-occurrence of the term w and g will be greater than expected; biasing the distribution. A term that shows co-occurrence with a particular set of frequent terms may have an important meaning in a document. Therefore, the degree of bias of co-occurrence can be used as an indicator of term importance. This bias is a measure of the chi-square value of the term w and is given by:
χ2(w) =
g∈G
(f req(w, g)− nwpg)2
nwpg (2.5)
Where pg is the unconditional probability of an anchor term g ∈ G, nw is the total number of co-occurrences of term w with anchor terms in G, and f req(w, g) is the number of sentences in which w occurs with g. Thus, f req(w, g) represents the observed frequency, and nwpg gives the expected frequency of term w.
However, to take account of the fact that a term that appears in a long sentence is more likely to co-occur with an anchor term than those that appear in short sentences, the terms nw and pg are revised: pg becomes the sum of all terms in sentences in which g appears divided by the total number of terms in the document; and nw becomes the total
2.2. Approaches Resulting in Knowledge-poor Representations 28 number of terms in sentences in which w appears. This approach does not make use of a corpus which is a desirable feature for domains like the SmartHouse where the document collection is small. However, it favours large documents where high term frequency leads to meaningful values of the chi-square.
The above techniques do not consider semantics and word order when ranking terms or documents. Hence systems based on these techniques are mainly retrieval-oriented where the task is to identify those documents or texts that are relevant to a user’s need. Typically, the derived case representations are knowledge-poor and systems make no attempt to understand case content. In many situations, original documents or their summaries are considered as cases. The developer thus incurs no case authoring cost and only an indexing strategy needs to be determined. However, the user needs to have a source of domain-specific knowledge for case adaptation. Such systems are not very effective in domains like SmartHouse and law where it is important to capture context.
2.2.4 IR-based Approaches in Textual CBR
SPIRE (Daniels & Rissland 1997) is an information retrieval system that was applied to the domain of personal bankruptcy. During problem-solving, SPIRE makes use of two case-bases to automatically generate queries which are used by a retrieval engine to retrieve whole text documents and to highlight those passages that are likely to contain useful knowledge for problem-solving. One case-base contains solved past problems which are represented as feature case-frames. Each case feature then has a case-base of actual text excerpts containing information pertaining to the feature. A simple indexing strategy is employed where the names of the features are used for indexing.
The Chi-square technique introduced by Matsuo & Ishizuka (2004) was applied to documents in the SmartHouse domain in order to identify representative features for the cases. The technique identified some key words that the expert had also deemed to be key but, it failed to pick out important low-frequency words and was worse when two or more word phrases were used as these were even less common than words. However, it performed better than the TFIDF technique as the Chi-square technique does not rely on the availability of a corpus whereas TFIDF tends to degrade with small document collections.
2.3. Approaches that Result in Knowledge-rich Representations 29