# Automatic segmentation of text into structured records

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4 Start 0.20 House No. Road 0.38 State City 0.05 Area Building Name 0.21 Landmark Pincode Figure 2: Structure of a typical naive HMM mold uses the example segmented records to output a model that when presented with any unseen text segments it into one or more of its constituent elements. 2.1 HMMs for text segmentation The basic HMM model as described in Section consists of: a set of n states, a dictionary of m output symbols, an n n transition matrix A where the ij th element a ij is the probability of making a transition from state i to state j, and a n m emission matrix B where entry b jk denotes the probability of emitting the k-th output symbol in state j. This basic HMM model needs to be augmented for segmenting free text into the constituent elements. Let E be the total number of elements into which the text has to be segmented. Each state of the HMM is marked with exactly one of these E elements, although more than one state could be marked with the same element. The training data consists of a sequence of element-symbol pairs. This imposes the restriction that for each pair e, s the symbol s can only be emitted from a state marked with element e. In Figure 2 we show a typical HMM for address segmentation. The number of states n is 10 and the edge labels depict the state transition probabilities (A Matrix). For example, the probability of an address beginning with House Number is 0.92 and that of seeing a City after Road is The dictionary and the emission probabilities are not shown for compactness. Training a HMM. The parameters of the HMM comprising of the number of states n, the set of symbols in the dictionary m, the edge transition matrix A and the emission probability matrix B are learnt from training data. The training of an HMM has two phases. In the first phase we choose the structure of the HMM, that is, the number of states n and edges amongst states and train the dictionary. Normally, training the dictionary is easy, it is just the union all words, digits and delimiters appearing in the training data. In Section 2.4 we present further refinements on the dictionary. In the second phase we learn the transition and emission probabilities assuming a fixed structure of the HMM. We first concentrate on this phase in Section 2.2 and discuss structure learning in Section 2.3. Using the HMM for testing. Given an output symbol sequence O = o 1, o 2,..., o k, we want to associate each symbol with an element. Since each state in the HMM is associated End with exactly one element, we associate each symbol with the state that emitted the symbol. Hence we need to find a path of length k from the start state to the end state, such that the i th symbol o i is emitted by the i th state in the path. In general, an output sequence can be generated through multiple paths each with some probability. We assume the Viterbi approximation and say that the path having the highest probability is the one which generated the output sequence. Given n states and a sequence of length k, there can be O(k n ) possible paths that the sequence can go through. This exponential complexity is cut down to O(kn 2 ) by the famous dynamic programming-based Viterbi Algorithm [27]. Readers familiar with the algorithm can skip the next section The Viterbi algorithm Given an output sequence O = o 1, o 2,..., o k of length k and an HMM having n states, we want to find out the most probable state sequence from the start state to the end state which generates O. Let 0 and n + 1 denote the special start and end states. Let v s(i) be the probability of the most probable path for the prefix o 1, o 2,... o i of O that ends with state s. We begin at the start state labeled 0. Thus, initially v 0(0) = 1, v k (0) = 0, k 0 Subsequent values are found using the following recursive formulation: v s(i) = b s(o i) max {arsvr(i 1)}, 1 s n, 1 i k (1) 1 r n where b s(o i) is the probability of emitting the i-th symbol o i at state s and a rs is the transition probability from state r to state s. The maximum is taken over all states of the HMM. The probability of the most probable path that generates the output sequence O is given by v n+1 = max 1 r n a r(n+1)v r(k) the actual path can be gotten by storing the argmax at each step. This formulation can be easily implemented as a dynamic programming algorithm running in O(kn 2 ) time. 2.2 Learning transition and emission probabilities The goal of the training process is to learn matrices A and B such that the probability of the HMM generating these training sequences is maximized. Each training sequence consists of a series of element-symbol pairs. The structure of the HMM is fixed and each state is marked with one of the E elements. This restricts the states to which the symbols of a training sequence can be mapped. Consider two cases. In the first case, there is exactly one path from the start to the end state for all training sequences. The second case is where there is more than one valid path. All HMM structures we discuss in the paper satisfy the first condition of having a unique path. Hence we do not discuss this case further. In the first case, the transition probabilities can be calculated using the Maximum Likelihood approach on all training sequences. Accordingly, the probability of making a transition from state i to state j is the ratio of the number of transitions made from state i to state j in the training

5 data to the total number of transitions made from state i. This can be written as: a ij = Number of transitions from state i to state j Total number of transitions out of state i The emission probabilities are computed similarly. The probability of emitting symbol k in state j is the ratio of the number of times symbol k was emitted in state j to the total number of symbols emitted in the state. This can be written as: b jk = (2) Number of times the k-th symbol emitted at state j Total number of symbols emitted at state j (3) Computationally, training the A and B matrix involves making a single pass over all input training sequences, mapping each sequence to its unique path in the HMM and adding up the counts for each transition that it makes and output symbol it generates. Smoothing. The above formula for emission probabilities needs to be refined when the training data is insufficient. Often during testing we encounter words that have not been seen during training. The above formula will assign a probability of zero for such symbols causing the final probability to be zero irrespective of the probability values elsewhere in the path. Hence assigning a correct probability to the unknown words is important. The traditional method for smoothing is Laplace smoothing [18] according to which Equation 3 will be modified to add one to the numerator and m to the denominator. Thus, an unseen symbol k, in state j will be 1 T j +m m j x m m j assigned probability where Tj is the denominator of Equation 3 and stands for the total number of tokens seen in state j. We found this smoothing method unsuitable in our case. An element like road name, that during training has seen more distinct words than an element like Country, is expected to also encounter unseen symbols more frequently during testing. Laplace smoothing does not capture this intuition. We use a method called absolute discounting. In this method we subtract a small value, say x from the probability of all known m j distinct words seen in state j. We then distribute the accumulated probability equality amongst all unknown values. Thus, the probability of an unknown symbol is and for a known symbol k is b jk x where b jk is as calculated in Equation 3. There is no theory about how to choose the best value of x. We pick x as 1 T j +m. 2.3 Learning Structure In general, it is difficult to get the optimal number of states in the HMM. We first present a naive model and later present a more elaborate nested model that is used in datamold Naive Model A naive way to model the HMM is to have as many states as the number of elements E, and completely connect these E states. To this we add a start state with transitions from it to every other state, and an end state with transitions to it, from every other state. In Figure 2 we presented an example HMM for a typical address dataset of an Asian metropolis. For simplicity, only the important states have been shown. As mentioned earlier, the numbers on the edges represent the corresponding transition probabilities. The self loops are used to capture elements with more than one token. This model captures the ordering relationship amongst elements. However, because it has just one state per element, it ignores any sequential relationship amongst words in the same element. For example, most road names end with words like Road, Street, or Avenue. Treating an element as a single state does not capture this structure. Also for country names like New Zealand both New and Zealand will be outputs of the same state. This state will accept Zealand New with the same probability as New Zealand. The other problem is that it learns only a limited kind of distribution on the number of words per element. For example, if 50% elements have one word and the rest 50% have three words each, then the naive model will create a single state with a self loop of probability 0.5. This accepts elements of length 1, 2, 3... k with probability 1, 1, respectively. In contrast for the training k data the corresponding probabilities are 1, 0, 1,..., 0. We 2 2 overcome these drawbacks in the next model Nested model In this model, we have a nested structure of the HMM. Each element has its own inner HMM which captures its internal structure. An outer HMM captures the sequencing relationship amongst elements treating each inner HMM as a single state. The HMM is learnt in a hierarchical manner in two stages. In the first stage we learn the outer HMM. In this stage, the training data is treated as a sequence of elements ignoring all details of the length of each element and the words it contains. These sequences are used to train the outer HMM. In the second stage we learn the structure of the inner HMMs. The training data for each element is the sequence of all distinct tokens (word, delimiter, digit) in the element. Start Figure 3: A four length Parallel Path structure S Figure 4: state path E S Merging a four state path with a three An element typically has a variable number of tokens. For example, city-names most frequently have one token but sometimes have two or more tokens. We handle such variability automatically by choosing a parallel path structure (Figure 3) for each inner HMMs. In the Figure, the start and end states are dummy nodes to mark the two end points of an element. All records of length one will pass through the first path, length two will go through the second path and so on. The last path captures all records with four or more tokens. We next describe how the structure of the E S End E

6 3-digits Figure 5: Numbers 5-digits All Others A.. Chars Words..z Multi-letter aa.. Delimiters., / - +? # An example taxonomy on symbols. HMM in terms of the number of paths and the position of the state with the self loop is chosen from the training data. Initially, we create as many paths as the number of distinct token counts. This might leave some paths with insufficient training examples resulting in a poorly trained dictionary. We merge such paths to its neighboring path as follows. Starting with the longest path, we merge each path to the next smaller path as long as it improves the objective function described in the next paragraph. Merge of a k state path to a k 1 state path as shown in the example in Figure 4 requires us to pick the one state that will not be merged. We try out all possible k choices of this state and choose the one that gives the best value of the objective function. At the end we pick the largest path and replace any parallel path within it with a self-loop (as shown in the last diagram in Figure 4) so that paths longer than the longest path in the training data can be accepted. Objective function. An inner HMM is good if it accepts the part of the symbol sequence belonging to itself and rejects the part not belonging to itself. Therefore, the best inner HMM cannot be found independently for each element. Another subtlety in choosing a good structure for the inner HMM is that, an inner HMM does not need to learn to reject all tokens only the tokens that belong to an adjacent element. We therefore learn each inner HMM in conjunction with others that are adjacent to it. Initially, all inner HMMs are unpruned. Starting from one end, we pick the HMM h of the first element and HMMs of all other elements that transition to and from this element. We first attempt to prune h. For this, we truncate the training data to the part that is relevant to all the selected elements. Two paths of HMM h are merged only if the accuracy of segmenting the training data does not decrease. After pruning h to the right size, we prune the next inner unpruned HMM that it points to in the same manner and so on. 2.4 Hierarchical feature selection One issue during the training phase is what constitutes the symbols in the dictionary. A reasonable first approach is to treat each distinct word, number or delimiter in the training data as a token. Thus, in the address New Hamshire Ave. Silver Spring, MD we have 10 tokens: six words, two numbers and two delimiters, and.. Intuitively, though we expect the specific number to be unimportant as far as we know that it is a number and not a word. Similarly, for the zip code field the specific value is not important; what matters perhaps is that it is a 5-digit number. How do we automatically make such decisions? We propose that the features be arranged in a hierarchy. An example taxonomy is shown in Figure 5 where at the top A 0.7 C 0.2 B 0.6 C S E Figure 6: An example HMM to motivate the need for feature selection. Numbers A.. Chars..z All Words Multi-letter aa.. Delimiters Figure 7: Taxonomy of Figure 5 after being pruned for best performance. most level there is no distinction amongst symbols; at the next level they are divided into Numbers, Words and Delimiters ; Numbers are divided based on their length as 3-digit, 5-digit or any other length numbers; and so on. Such taxonomy is part of a one-time static information input to datamold. The training data is used to automatically choose the optimum frontier of the taxonomy. Higher levels loose distinctiveness and lower levels are hard to generalize and require more training data. We need a middle ground. We motivate the need for feature selection through a small HMM in Figure 6 with three states and a start state and an end state. The dictionary of each state and the associated emission probability is shown in the box above each state. Thus, the dictionary of state 2 has two symbols A and C with non-zero probability 0.7 and 0.2 respectively whereas for state 1 only the three numbers 3, 45 and 66 had nonzero probability. Each state assigns the same probability of α = 0.1 to unknown symbols. Suppose, if we get a test sequence of the form (90, D). Then, this HMM will assign it to states (2,3) because the emission probability of symbols 90 and D that are unknown to all states will be the same and transition probability of the (2,3) is highest. Intuitively, however we expect digits to be assigned to state 1. Suppose instead through feature selection we transformed all individual numbers to a special token # that stood for all digits. Then dictionary of state 1 contains just one symbol # with probability 0.9. Then the probability of the path (1,3) becomes that is higher than that of path (2,3) at In contrast, if we attempt to convert the symbols A, B, C to a single denoting all letters, then the dictionary of both states 2 and 3 become the same containing a single with probability 0.9. In this case, we have lost the distinction that in state 2 letter A is more likely and in state 3 letter B is more likely. In the extreme, if we replace all numbers and letters by the single symbol All at the topmost level, then it is like not having a dictionary at all. The best path is chosen based simply on the structure of the HMM and the

9 Element Tokens Precision Recall present (%) (%) House No Po Box Road Name City State Zipcode Overall Figure 8: US addresses Element Tokens Precision Recall present (%) (%) CareOf House Address Road Name Area City Zip Code Overall Figure 9: Company addresses Element Tokens Precision Recall present (%) (%) Care Of Department Designation House No Bldg. Name Society Road Name Landmark Area P O City Village District State Country Zipcode Overall Figure 10: Student addresses Figure 11: Precision and recall values for different datasets shown broken down into the constituent elements Accuracy Naive HMM Independent HMM Rapier DATAMOLD Accuracy No feature selection Digits collapsed Numbers collapsed Numbers+Characters collapsed 20 0 Student Data Company Data US Data 0 Student Data Company Data US Data Figure 12: Comparison of four different methods of text segmentation able on the internet 5 ). Rapier is a bottom-up inductive learning system for finding information extract rules. It has been tested on several domains and found to be competitive. It uses techniques from inductive logic programming and finds patterns that include constraints on the words, partof-speech tags, and semantic classes present in the text in and around the tag. Like the Independent-HMM approach it also extracts each tag in isolation of the rest. Figure 12 shows a comparison of the accuracy of the four methods Naive-HMM, Independent-HMM, Rule-learner and datamold. Accuracy is defined as the number of tokens correctly assigned to its element as a fraction of the total number of tokens in the test instance. The number of training and test instance for each dataset is as shown in Table 2. We can make the following observations from these results. The Independent-HMM approach is significantly worse than datamold because of the loss of valuable sequence information. For example, in the former case 5 November 2000 Figure 13: Accuracy by generalizing features at various levels of the feature taxonomy of Figure 5 there is no restriction that tags cannot overlap thus the same part of the address could be tagged as being part of two different elements. With a single HMM the different tags corroborate each other s finding to pick the segmentation that is globally optimal. Naive-HMM gives 3% to 10% lower accuracy than the nested-hmm approach of datamold. This shows the benefit of a detailed HMM for learning the finer structure of each element. The accuracy of Rapier is considerably lower than datamold. Rapier leaves many tokens untagged by not assigning them to any of the elements. Thus it has low recall. However, the precision of Rapier was found to be competitive to our method 89.2%, 88%, and 98.3% for Student, Company and US datasets respectively. The overall accuracy is acceptable only for US addresses where the address format is regular enough to be amenable to rule-based processing. For the complicated sixteen-element Student dataset such rule-based processing could not successfully tag all elements. 3.4 Effect of feature selection In the graphs in Figure 13 we study the effect of choos-

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